{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Listed Volatility and Variance Derivatives\n",
    "\n",
    "**Dr. Yves J. Hilpisch &mdash; Wiley Finance (2016)**\n",
    "\n",
    "<img src=\"http://hilpisch.com/images/lvvd_cover.png\" alt=\"Derivatives Analytics with Python\" width=\"30%\" align=\"left\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Valuing Volatility Derivatives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Valuation Framework"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import math\n",
    "## model parameters\n",
    "v0 = 17.5  # initial level of volatility index\n",
    "kappa = 0.1  # speed of mean reversion\n",
    "theta = 20.0  # long-term index level\n",
    "sigma = 2.0  # volatility of volatility\n",
    "zeta = 0.0  # factor of the expected volatility risk premium\n",
    "r = 0.01  # risk-free short rate\n",
    "\n",
    "## option parameters\n",
    "K = 20.0  # strike\n",
    "T = 1.0  # time horizon in year fractions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Futures Pricing Formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('scripts/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.7379064549101"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from srd_functions import *\n",
    "futures_price(v0, kappa, theta, zeta, T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns; sns.set()\n",
    "import matplotlib\n",
    "matplotlib.rcParams['font.family'] = 'serif'\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "maturities = np.linspace(0, 2, 24)\n",
    "futures_prices = [futures_price(v0, kappa, theta, zeta, T)\n",
    "                 for T in maturities]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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NZ9lSXU/1uy0kUykmjC3k0dVVrF1UzpSS4myXJw0ZA5wkKaddmlR+y74GjjT3TipfXjqe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\nMcBJkiTlmP8OHQ2ETERcsSMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d45e350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6));\n",
    "plt.plot(maturities, futures_prices);\n",
    "plt.xlabel('maturity');\n",
    "plt.ylabel('futures price');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Option Pricing Formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3682878822902369"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call_price(v0, kappa, theta, sigma, zeta, T, r, K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "option_values = []\n",
    "strikes = np.linspace(15, 25)\n",
    "option_values = [call_price(v0, kappa, theta, sigma, zeta, T, r, k)\n",
    "                 for k in strikes]\n",
    "data = pd.DataFrame(option_values, index=strikes,\n",
    "                    columns=['call values', ])\n",
    "data.index.name = 'strike'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XlyciIiK91Ks5U4OhsbHVNV/4IunLcGdbewdvrN97Zm+/TvKyorlpYTqhQX6DXKWci6cP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d45e050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monte Carlo Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## simulation parameters\n",
    "M = 50  # time steps\n",
    "I = 20000  # number of MCS paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "paths = generate_paths(v0, kappa, theta, sigma, T, M, I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cpNf1Cebi7XmdQ1EUXv31GRLxNPc85CSZTi9ofNYiPU9+YxeHP+rm4oUxfv6D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\n1bXL5Tp34YF+Jh77ObI0E/l//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L0WWfSpKxWGW2/HUV7O8Ltvo5lXgCRCN+m9a2Rqbkq/\nhlUJl7Grcx8He46wseEddnbs5crklSyMKZhUkVcklaGZX8jo/n04a2tQ5c6iaFczXo+fy67MYGZe\naEXU5/Mz7vIx7vQy7vr7y+3y4nJ5qa/s5+AnTcQl6VBdxIy6qVBR0k1NaS9RJg1rrp/5hRpVwWCQ\nMZ+TMIlyyoNdvaWJHR17iVTouCvr5im5qTUyNTmRWVQN1dLj6CNOPb1i65e4xIUSDAQwv/IitkMH\nkSckEv+Tn05Lr+kS50/raAejHhuLTPORfAPcgHCRV6w+w6DU02tSYG2pJ7zNjM9uQzV7DmFSJXnG\nXCqHahlX9uAL+CktDVCYHX1GPavPEIsE/IKXBmEPHb5aJIKEAAEyden0OwdYGreQJbELmGPMZVHs\nfArag2S9UcTC5EUUzL+aFG0Sg85haobrOXhslK17rbT329GqZFwxP5EHrpnJ5flxxOjDEJ30cAhT\nSMhK0rFyVhSKD17HF6am1SmhvGmI3Se6Gba5USulvHewjfcOtSGViHjgmpmsW5iEWCwiGAxifuE5\n/E4npgcfRqSYfIVu055mSq1iFjga8fb2ELFyNWKJmPAwGRqVnKi5c7DX1hLd34S1pZX01cuRy6VI\nJWI8jfX0/ekPUFuBTCZlMGsBL6sKORSWgSUilvzrV6OUnx6wrw4Pw6uKwF58BE9vT0jY7aR7f7f5\nY/qdA9yZdTM6RQQR+jCMMRqa6gZoqRsgKkKE443nEWRyYr//+KTK9SKVCseJ43gtVjQF88/axwqJ\nnJmRmSwyzScQDNA00krFYA0l/WXIxXJiVaevYInVamyHDkAwiCUihZIDbcTEh7Nszd8LiopEImRy\nCWFqGeERSvRGFVGmcGITI0hMjUSukNDaMMToiIv07KivrSp4S/0A+7c3olLLuPfRJfAF2VT9Y2b2\ndx9mY+N7vNeylV2d+ykdqKTJ2krvmBm7x04gGEAhkZ9i4No9DjaUP4834OXROd+bVuFtkSCibKAS\nuVhOtn56cZPfBi6tWF08goEA/S8+j/1wEfKk5GkbVZf67sLY1bmfDlsXN6StwxgWee4DvmC+/srr\nZzCsAFIjUtgXPkhYcy+ShlbESiXKtHSUEgX5UbOoHqpjXNnDuM9DdaXAwpkxZxXAHHJZ+KBvI07J\nAEF7JAHROPnRuaxLWc3h3hICwQD5n/prg8Eg5pdfIGi3k3r/Y0Rq46ivD1J2AtB14w7rIV2TzvoV\ns/nOmkyyknTnNuxkMpT1ZaQ3HOKaR29HFaGhd2iM+g4rByv76BxwkBSj4ad35JGR8PeVGWdtDSOf\n7EBTuBDtkqWTnnvA6uSlbQ0YjeFclqrCVVeLPD7+FFVxQSRCVzifmkNlRA934OjuQZ2Ty+CmNxh8\n83UC4+PorlxH7KOPk7iogMg9W+j3y2mVRnGwsh+VQkJijOYUo0GlkuPT6Bnv6sRZUx3KUklMBEIi\noBsb3yVObeLa1CsmjjvZuKJoOxp7H4abbjmj7oo8IRFnbQ3OmioUqanIos6dPvt3A6sAf9BPk7WF\niqFqSgcqydClo5H9fYVTotNhLylmvLWN4lETXh9cdfMswqZRX9IYo6G3c4SuNis6gwq98etXJLi/\nZ5Tt79QgkYi49o45xCfpL2hwH3AOcbDnCJsa3uPjtk9oGmnF43eTqUtHJgqppHc7emkaaaVssIpD\nvcXs7NhLcd9xaocb6bR3s7frEP1OM9enXcW86OnVbTQo9OzvOYJ5zMyKhGVfW2P2y+LSw/niEPT7\n6f/bs9iPFodquf7kScSq6YUAXOq78+czN6BEJOH2jBu+cjcgfMMNK0EQyI7KZou4joSWEbwVVbh7\ne1BmZBGm0oaMq+E6XIoe7ONOWhvlFGZHTyoa2mht4U/lz2F1j5AsmUV/eQaIAzyw8CoSNHGcGKig\nzdbJZXGLkYqljLc0Y9n6Eer8uXTFz+KpLZUcqx9AIVaSl5BCf7AJccQw12YvRS6ZetBywONmrKIc\nTUwUeSsKWFOQQIopHJ8/QF66gQevzSH8c0HQg2++hnfATMx93zujK2zTnmY6zHbWr8kgJSeNkT27\n8FksaJdddsp+IrGYLkMq1upa1N3NjOzZzXhrC7K4eOJ+8CO0i5cgkkqxbt9KsOQQS+cmEbdoPrXt\nFk40DFLbYSXVFD7Rxs8GCGVaOqMH9uFqqEe7dDkimYzdnQdoGmnhmtS1JIafmnEXoQ8jUuoirOh9\nXNJwJFffgVY/uTESqiGVwujB/Yw3N6FdfvmUU14VEgU5kVnMV2UwUldFi3SUo30niFObJtTbBUEg\n4HLhrK1mJBBG2rI5ZOROT5JCEARMCVrqKvrobreSNTsGqfTrU4Nw1Orkwzcr8Xn9XHFTLrEJEec1\nuA+7LBT1HmVz0/t80LqdRmsLLp+LHEM265JXsz7rFhbHFrI0buGnWZrzydFnkRgeT6RSj1wsw+6x\n0+3oncjqmanP5LbMG6ZtGIlFYoZdFppGWknTJl+UmevF5NLD+asn6PPR99xfcRw/hiJ9BnE/evKs\npVHOxKW+O3+aRlo50HOYwph85hhzL0obvtGGFYBUJCU5egavi6qIsviQNXdiKzqERK8nPDGV/KjZ\n1AzX45T3Mmi3MdyjIS/91EKPB7qP8GLtG/gDfu7MvIlsYypHqyyInJHcvDgXqUTMuM9NnaURozKS\nxPB4ht7ewoB5hG0pa3n/aA9Ot49V8+J5/KZZLExLQwAqh2rosvdQEJ03ZatZotNj3bmdoMeNdmlo\nlh2jD6MwO5qcFD3izxmFnv5+Bje+jiIt/Yx1n8xWJy9va8BkULF+bQYSjYbx9jZc9XWE5eQi1Z8a\nKB+jkVB3uAzDuAX8fuRJyST8y8+RRYbcMN6hQfqe+QviMBVxjz9BamIki3NNWGzjVLdZOFDRi88f\nJD0uHI1GgdPpQRwWFqrtVF6G22bHmTyDjc1bECGwUL2WAasblUKK7CRjw/nuG/j6+2iIWUJlm++U\ngPbTPjetlsC4i7HKShAgLGvqqsKO8jKG//Q0iXUD6Gw+WmKlHBssRyqSkqoNCc06UDBetA+54GPu\n925CLJ7+LEihlCKRiGhrGsJhd5N2HiWUvgxcTg8fvFnBmN3NZVdmMGNmaMVvqoO7x+9hf3cRW5o+\n5N2Wj6m3NuHwjpEdmcGVyatYn3Uri2LnE6c2nRLvIAgCSokSY1gkKdpEcg3ZLDQVsCrxMlbEL2WO\nMZcsfTprk1eet4J6mCSMI33HCAJ5URdnkL1YXHo4f7UEvF76nvkzY2WlKDMyif/hT85bzftS350/\nOzv30Wnv5oa0dRiUF2cy9Y03rADCZRqkag2bNK3odCYiOy04So7i7upEN3M2BYmFVA+FjKuu4WGC\ntigyE3X4A37eanyfre2foJKG8eic+5ljzOH1xs1YXTZ8IwZUCinp8Vr0igj2dRXh9DmZLU/n3Y9O\n8EH0MvrHAmQkRPCDm2ezdLYJ2af109IiUuh29FJracAb8E05xkMkl+Osr8PV3IT2ssvPWcZg6P13\ncbe3Ybz9jjMWC960p4lOs4P1azKIN4aWpCXaCGyHiwiMj58SlzRWXUnfU39Ab+1hUKZFqlLBQB/4\nvITNzEEQBPpfeB5PTw/Rd987kYmnlEuYnx1NUrSGxu4RKpqH2VHSxfYjHWwt7uCDojZ29AjMcHQi\na2/kbz1DjEYN4u6Lp+hwkOIaM7tLuxkd82CKDENob2b4nS0oMzIx3XkHLXUDNH0uW/DzKNPSsRUf\nZqy6Gk3B/HPGNAQ8HgY2vsHQW29CIIDx9juJVUYRdaiWjkQVlbZGhsYtzNRlsHtHB1JzBxHOfnRL\nlyFWnZ8rL8oUTlebha5WK8ZoNRGRp89mB5yDyMXyKRnjjsoKCATOOyjW5/OzdXMVwwNj5C9MJH9h\n4sS2qQ7ubza8w87OfYy6bWTq0rkieQXrs29laewC4jWxSM/DKJKKpegUWmLVMRekQaOTaynpL6XT\n3sXl8Uu+MYGsXwSXHs5fHQGvh96nN+CsrCAsOydUh+88StB8xqW+Oz/8AT+v129BJpZxW8b1F8UN\nCN8SwwogQRNHi62dYlk/2StuIMIyjrOmmtFDB1Hpo1g092pqhhtwyntp6OtHHTDyQe9mygariFOb\n+GH+w8RrTDRaW9jesZvchFis3Xra+u2snBuHWh5Go7WNhrYxDu7z0ySLQSMTuPeaHG5fmT6he/UZ\ngiCQE5lJ+WA1VUN1xIQZz6m98xkBlwtndRXS6GgUySln3M/vHKP/heeQaLVEf+feSdNuP1utiv10\nteqzlTpJpIGx8lJcDfWEL1kKBBl47VWGNm8i4PWgufIanhbl0RSRxnzxEGMV5aG2jY9j+eA9lBmZ\nGG+/8zT3TExkGHqNghONA/j9QVweHyqFBJ1ajkEXhkcXTWJvLXFeC1VpMuar15KXEsuMBC1mq4va\ndiu7j3eRfGALcvcYsd9/HENqHEaThubasxtXgkSC1GDEfrQYd28P4YuXnNF95O7uoucPv8NZVYEs\nNo74Hz+JOi+fsJk5SJs6ST7cgjlVT/14F2U9NXgaVcQYtajMLYjVasIyp1/6CEL/FzGxIZdgb2eo\n+PTJxYwrBmv4femfcfnGyYk8+zVGD+6n/9m/YDtShCJ9BtLI6Ql4BoNBdn9YR1erlfRsI8uvyDgt\nPu5c371Rt53X6t7CGBbJzxf8E8vjF5OoiT+jeOdXjSAIOH0u6q1NRIcZidd8O1WnJ+PSw/mrIeB2\n07vhKZw11YTlziL28R9csDDnpb47PxqtLRzqLWaBqYDZxotXC/F8DKtzmoCZmZnRmZmZz2VmZpac\n9N4vMzMz95z0WjXdC58NQRBYn3ULMrGMTcP70TzxKFHr7ybo99H/wnPY/vosj6feTrQiBklUN5vN\nz9E00kqeMZefzH2USGUoNmlb+y4Arp2xklXz4rGNeThY2Ufv0BjDVVl4mvOxewUW2ur4P98rZOHM\nmDM+vJUSJQ/Puhe5WMZrdZvpcfRN6V4+E39zlJ44636jBw8QdLuJWLH6jDFFHx1uJxAMct3SlFMy\nEQVBIGL1FRAMYn71Fdp/8W/YikJpwYn/9gtMN9/MsrwE+t1i+tbdi9RoxPLh+/Q//wyIxUR9555J\n7/tIdT/PfFCDWCRi+RwTwWDoWj+8dQ7/+p153PfI1UgK5xE5Ms7aLhX3r5rPDctSuWl5Gr9+eBEP\nXTuT5fQQbhugWpPK/xZZKW8eIj5Fz5U350IwyNYtVbTUDzJZzUp1/lxUefm4GuqxFx8+bXswGMS6\n+xM6//tXeHp7iFi5isSf/xJ5fEjcVRCJiHngIfQxSdzwbie5nigGfGZacw4TeX0egkyG7XDRpNee\nKnqjinlLkhhzeDiyt3Xi/VG3nTfqtwBQ1FPMiHv0jOdwNjZgfu0VREolAa+Xnj/+jrGa6mm14+j+\nVlrqB4mJ17Li6qzzCu4+2HMYf9DPivhlp6nlf11YEBP6Ph3tL73ILbnEt43A+Dg9T/0hJMUyJ4/Y\nx35wRm2qS3z5lA5UAN8cUdCTmcra2hLg/c+9F2xoaFh50mv3F92wSKWeG9LW4fS5eKv5fbSXryT5\nV/9NWHYOY1WVDP3Xf/OQdzaRkmgEsZ9ozxy+l/sdFJLQ7KLJ2kLTSCs5kVkkhSewZn4CMqmIdw60\n8ssXSujp86GRD/BA5/tcn61GFXHuB0mMKpp7Zt6BJ+Dl2cqXGfOeu46hNNKAPCkZZ30dfucYgWCQ\nD4va+KCojdGx0Cwm6PczsmcXgkyGdtnkytJmq5Mj1WbiDCrmTRLPoylcgFirxVldid9uJ/KGm0j8\nt1+gSEwCYE1BAiJBYFvtKHE/+RkSnT6UGbjmikndjjtKOnnuo1rkUjH/dHse370qm7vWZjI0Os7v\nN5XjcHkBODHfiEsmkHmsB6/FMnG8RCyiMF3H0qFSghIJ/XkraOga4aktlfz780fpsLtZfUMOBIPs\nfK+Gd14ppaNl+DQjJ+rO9QgyGYNvbcTvcEy877PZ6H3qDwy++ToihZLYJ35E1F13TyrhEPfED1Ho\nIok+oCW6MwuvbJy/tr2KOzsF7+AA4y3N5+xH+FS3qbaGwU1v4h0anHg/f2EikUbVp8HsFoLBIG/U\nb8bhHWNGRCq+oJ9dHfsnPad3cJC+P28AIPaxHxD72BMQCND7pz/iKC+bUrtqynopK+5Cq1Ny1c25\np6yaTRWP38vBnmLCJEoWmKZX4PqrxKCMJFWbTJO1Bev4yMVuziW+pgS8XgbeeJXu3/+Gvmf+jPm1\nVxh6920sO7YxeuggjrJSnI0NuHu68Y1Y8dvt9Pzv73E11KPOnxeSg5lGjdhvC76Aj+erXuU3xzew\nvX03vY7+C5p4ni/+gJ/ygWrCZRrSI87s6fm6cs4ghYaGhncyMzMv+9zbQmZm5r8CbkAM/KmhocH1\nRTduWdxCygYqqRispnSggnnRecT95ElsBw8wuHkjtldf476Z2byhWUKrRUN/gYtYQyheZmt7yNa7\nKjm0mBYeJmNFfhw7SrowaBXcuXoGno2/Rue1YyvIZKo1sfOMuVyZtJLtHXt4qeZNvj/nvnP6ftVz\n5+HuaMdRUc7WsSg+Od4FhFagCrOjWakcxjc8jPayFWcsifBR0eSrVZ8hkkqJumM9tqNHMNxw08Sq\nzWdEahUUzoyiuMZMvU3A+8jNbGvfzT35C07ZLxgMsnlfC9uPdhKhlvGT2/MmYrnuWJvJwPAYu050\n88fNFTx+SxZHbNWMzY9icZGZwU1vEPv9xyfOZd25Hf/ICJFXX8ujNy6ja8DB9qOdlNSZeXFbPVqV\njGV5JhQ2Nx1Nw2zdXEWUSUPBkmQS0/Sfln0wEHndDQxteYuhd7YQfc93Gauuov+F5/DbbITNzCHm\n/geRRJxe0PozJNoIhFsfwbyrh9SOYdblr+HNkYN8rDdzIzB6+BDK9BlnPD7gdmMrPszI7k/w9PYC\nYD9eQvxPforMFItYLGLF1Vm8/fIJ9m1rJH6dn+rherJ0M3hkzn38Z/FvONRbzJqkFafUuvO7XPT8\n6Y/4HXai7r6XsKyQKGvcD39Cz5/+SO9fNmB64GE08wvP2LaOlmEO7mxEoZRy9W2zUCjP72FwzFyK\nwzvG2qQVyL8mrr8zsSBmLq2j7RzrL2Nt8oqL3ZxLfM0IBgL0P/8MjhPHp32suqAQ0wMP/UOWlQkG\ng7xZ/w5lg1VAqD7fh607MCojmW3MYY4hlxRt4lcS61QxVMOYz8ll8YsvWmzVhTClGKsNGzYkA2uf\neOKJ5z79ewDY3tDQcGDDhg2zge898cQTH0zhelOKsfoMQRBI1SZzuLeEBmszC00FyCVyFEnJaBYu\nwtPXj7umhpkDbdgEOS1+NQVZ0TSPtPFx206y9Rlckbxy4nyZCTpSY8O5fWU6UX4H7nc/pMcopX5O\n1LR8uDN0qXTYu6i1NBAMBsjUp591f7Faw+je3fRbnGw0a4g1qLhmcTIDVhd1HVbiS7ah9Y1hWXUz\n0XGnC06arU5e3t5ArFHFXWsyzujmkcfFEV64EEm4dtLtxggl+8p7GXaOcIydWINOmkbbWGiah0Qk\nwecP8NLWevaW9RCjD+Nnd+UTc5IsgkolJyVazeDIOFWtw9TYyrFLu5mXvxZTrxNnTfWE9pRvxErf\nM39GrFIR+8ijCBIpWpWMeZlGls4yIRIEWnpHqe0epcPpIX9uHJEqGT0dIzTVDtDRYiFMLUOrU6JM\nSQ3NMKsr8fT3M/T2ZoI+H8ZbbyfqrrvPma3j9wXYvrUFj8vHbPNeIsqrWLruPo6JeklusODt6kKz\nciVS6amxFN6hQSwff0j/88/iOHEcv9OJpnABqtlzcFZVYj9egionN1TgWS3H5wvQ2NPBYekelFI5\nj+c/gEoahlQkoXIopOSfHRlKfAgGAvT99WnGm5uIWLn6lCxQqdFIWEYWjuMl2I8WI4k0TOiFncxg\nv52PN1ciCALX3D4bQ9SZV13PFucRDAZ5pW4TTp+L7868E6Xk/AN1vwoMSj17uw9hHR9hedyifwhN\nq0txOlMjGAxifvUl7MVHUGZmkfTLX6FbtZbwJcvQFC5AnTeXsNxcwjIyUSSnIIuLRxYVhUQbgbqg\nkOj1d3/hRtU3pe92dOxhT9dBEjXx/Gz+EyRo4hAh0OXopXmklSN9xzjUU4zZOYhIENDJIyatcHGh\ndNi6eK7qFQTgjsybCL/Ihde/spI2DQ0NdSf9uQd4cqrHGo3T+5CMaLhr/HpeLt/C+x0f8+PFD3y6\nQYPpv3/B4N59tD73AusGjtC4txv7ZYns7t0HwF3512E0nHq9WFPI6Gjd+k7oZ66BuqFKHtWtRzYN\njaonlz/Iv3zyP2zv2ENu/AwK4/POchOZtEZGoehsIiZ/Mf/n+0swRCi544psju85gfdPZlqVJt46\nMEhU9VGuWZLCmgVJqD9dfXhtVxOBYJC7r5pJdFT4lNt4WjOMGvJwJgFoAAAgAElEQVQyjNQGdiL2\nuUjRJdBm7eKd9g94aO49/N9XT3C8zsyMhAh++cDC04L4AaKjwvnZvfP5/18uoUI4hDgoYl3uShQ/\nyKf8xz9leOPrJDxVQOvGDwl6PCQ/eD/RCVGntSMzzch3r8tl25F2tuxp4t2STqL1Ydx0TRbOrlFq\nK/vYtqUaU7yW5WszyHj8Ear/5efYS4pRxsWS8eSPUaemTum+9+9oYNTionBZCvnqW2n+058JPvcq\n//Xf/862ql+TdLyLLR/8lnvu/zmRSh226hp6P9qKpeQYBAJItVribr+VmCuvQKYPxe/1JcXR+sxz\ndP/2f8j55c/RZGaw9rps3nO+hV/wcVPSLcyID+l5Xatfwc7OvRzqLebOudcQrtDQ/tIrjFVWEJE3\nh5mPP3R6XJ1xLjrjf1D7q//C/OLzqGQCpnVXTmwetbrY8U4NPl+AW++ZR/YUCpSf6btX2V9H35iZ\nJYkFZEyh+PjFR0NB7GyKu0txSKyk6pMudoO+EqY7dv4j0v7Ka9gOHkCVlkruf/wbkvPQnfoy+Lr3\nXVHnMT5s3YEhTM/PVzxOhFLLjPh4ruYyPH4v1eZ6SnoqONFTyeG+Eg73lSCXyMmPyWF58gIK4r6Y\nOKgeWz9/qXwBT8DLTxY/SH585hdy3q8aYSr+009dgb9taGiY/+nf/7ehoeFnn/5+LfBEQ0PD2ilc\nLzg4aJ92IwPBAL8/8RfabB08mHs3eVGzTtnuHR6m+ek/I+5swS1XsGOBHNmsHH6Q/9Dk53O7aX3y\nRwgyGdUPr2Fn9wHuy7mLguizGEeT0OPo47fHNyAIAj8reIIY1eQOxWP1A9T87VUWW6tQ3vMQCcsX\nT2zrf+E5bIeLUNz3KPscWg5X9eHxBZBLxSyZFcOcdAP/u7kSkyGMX91fOKkbcDp8WF3M9oF3CPMZ\n+f9W/5inyp6hdbQDrXUe/U1GclL0PHZj7ik1DT/DaNTwWf/VDjXxdOVz+IZNLFRdyXevymLorY1Y\nP9mBuqAQx4ljyGLjSPrlf56zsKjD5eXDonb2lHbjDwRJMWlYlx+Ppc1Cc10olskQrSZbM0IUVgzX\nXjflTJ0Ri5NNfzuGUinljgcLkcklDL2zBcvWj1CkzyDyzjvp+a//pCNGSm+6niWtIgK9ocQEeVIy\nutVrUBcUThpvYTtymP4Xn0eQSol74kfslXezte0TtEOxzBpZxK33F0zEO+3rLmJz4/usTVrB5f1q\nzC/+DWl0DIn/+u9nlXtwd3fR/bvf4LfbMNx6O7q1V9LRPMyhXc3YR8dZtCKNvAXnNoZO7rvP83TF\n36gdbuBnBU+QFP5NMKygaqiWv1a+xOXxS7g1Y3LNt28TZ+u/S4Sw7NjG0OZNSKNjSPjnf0USfv6T\n0C+Sr3vftY62879lzyIRxPzTvMfOmvEeCAZoG+2kYqiaisEahlzDAKxJvJzr0q68ILedZdzK7078\nmRH3KHdl3cyS2AXnPugrwGjUTPuhe05XYGZm5nLgHiBvw4YNyg0bNhwDlm7YsOHaDRs2zANWAU8+\n8cQTlrOeKMS0XIGfMeES7CuhwdLMwtiCU1LAxWFhRC5dyu6aYWIs7WS3j5MuMqLLmTPpsu5o0SEc\nJ46jW3slsXmLONBzGI/fQ+GnGUdTJVymwag0cNxcTr21iQUxc5GKTn0A17RZePrdKgIyObNGGlEq\nZWjmFgDgGx3F/PILSI1RJN1zN3kzjFyeH4daKaV7yEFtu5XiGjNB4O61mcQZp1dK4fM4vS5eb3kN\nt8+HozafJdnJpGlSKe47jkvRS44+iyeun3+KsOfJnLyk/X7rNvrHzOht86lpdOHxBpi3ej724iOM\nt4aCwWMeeAhZ9Lmj12RSMbNSI1k4M5rRMQ817VaONQ0hiVCyZmUa4iB0t1vptIjoDRoYGXXj9wUI\nU8vOGqgdDAb55P1abNZxVqzLxBgTmjUqM7Pw9vfhrK6CcTeCRIK6x0JC5xh+hwNJ/mzi73uQyOtv\nRJGQeMYsTXlCArK4eOzHjmI7eoTDQgcYIlkluYqeVhvBIMQnh1a4YlUmjvQdY6ypnoT3ihEpFCQ8\n+c+nCbp+Hkm4FnVeHo6yUhwnjtNYO8ChWj9ej5+CJUnMXZQ4JVfYmdwR/WMDvN30IanaZK5KWX3O\n83xdMCj0HOwppnesn5UJy76RcRjT4ZviTrpYjB46yOAbryLR6Un46T8j1U1eteJi8HXuu0HnMH8q\nfw5PwMtDs+4lNSL5rPsLgoBeEUG2PoPL45cw25hD40gLVUO1mJ0DzIrMPi/3oN3j4KmyZxket3B9\n2lWsSJi8nNvF4EtxBTY0NBwADnzu7X+b7oUulBhVFNekrOW9lq1safyA7+bcecp2kVhM5I0L2Vhf\nx1VFTiKPltLR+gtiHngIZdrfY6CCwSAje3eDSIR2+eVIVTqSwhOoszQy6rahlU9vljMveg6d9m52\nde5nY8O73Jdz18S2lt5RNrxThSAI3LF+BZI/H2assoKgz4cgkTC6fy9Bnw/d6jUTqzpqpZSrFiax\ntjCB0sZQ4eYwuYS5X4Cy93stH2Pz2JmjXkKxS82m3U10mO2Mi2YhzyhlxFCML1iIhLOvBo24R6kY\nrCZObeKx61fyP6+Xsb2kE5VSwvI77qLvLxsIy52FKmd66thRujC+f0Mua3tHeWtPM+XNQ1S2DHNZ\nXixXr8+jsbyX9qZhakp7qSntRRBCdfvik3XEJemIiQ8/xdBqqh2gp2OExDQ9qSd9foJIRPT9D+C1\nWLCXFKOeVxDS9MowsSmyk2D4KI8aZKRMwWDRzJ2H/7HH6X36KdbtH0Ecv4qUy7LobT5OeXEnaZlG\njDEaZGIpV4bPQ/vmZoJ+MD3yGLKYKZbS0RkZXL4exdaXMLQcIS/JQ8Yj9xF5gYY2wN7uQwBfq4Fs\nKohFYgqi89jXXUStpYFZhounc3OJi4uj7ATml19ApFYT9+Mnp60B93XC5w8gOY9KEOeD0+vkL5Uv\n4PCOcUfmTcyMnJ7bTRAEEjRx/NO8x3i28mVKByoZcdt4eNa9qGVTF10e943z54oXMDsHWZW4nDWJ\nl0/zTr5+fC0FQs9EcngCtcON1FoaSNTEER12qrGxvfdjOoIWSr2LWJaRjK++GtuhgwQDfpTpGQgi\nEePNzVi3fYR67jwiPq2t5wv4qRmuJ1yuIVWbPO12ZUSkUWdpotbSQHJ4IlFhBnqGxvjtm2WMe/08\nekMus9IMeIeHcDU0oJyRgUSno/+5ZxDEImLuf/C0lTWRIBBnULF0tokFM6MvOEC30drM5qYPiFOb\n+P7c9RypMdPWZ8fl9nPTglkkmBRUD9dhHR9ljjFn0ut9NvPa3XWQRmsLV6esJSMymfwZBo43DFDa\nOETUjGSylxcQsXL1eWvA6DUKls4ykRitod1sp7rNwpGGAVJmGLjh+pkkpxtQhysIBIIM9tvp7Rql\nsdpMRUk3vZ0jOB0egsEg+7Y2QBDW3Xp6tpwgFqPKy8NReoLx5iaMt95OynW3Ea4xUDpYyTFzGSnh\niRiUZ19RAnh35AhHFGayu32IymuRG42Y5mbTWG3G3Gsja7YJPG547nXEVhuHCnXMWnXLOVXMg8Eg\njTVmtr9dTWePizFTBiZfH2H9zcgCblS5sxAEgWAggN9uxzs4gKenB1drC866GsYqyrEdLWb0wD6G\nDx7EXluLu7MD7+AAfrudsXE7b7V+hCYsgjsybvzGrfqopSqKekvwBwPfSK2b6fB1XvW4mDjr6+h9\n+ikEqZT4H/900iSPi81U+66saZD/eOEY0bqwiUzsLwtfwMdfKl+k097DqoTlXHlSktd0kYllFETn\nMegaptbSQOVgDTMjM1FJzx3f5g34+GvlS7SOtrPQVMDtGdOvI/pl861RXj8TIkFEijaJI59mCS4y\nzZ94OHXaunm35WNM8niGm5Oxx6Ry+U2X46yvZayinLGqSsIyMrDu2I6np5uo9XcjNYQMM4NSz96u\nQ4y4R1kWt3DaHSsSRCSHJ1DUW0LLSBuZqtn8fmMlNqeX+9ZlsXBmaGVCkMmxFR1CJJcRdI1jP3qE\niFWrUc+ZXmzXdPH4PTxd8QIu3ziPzP4ukWE6VAopte1W7rkik9UFCWTo0qizhIxWnUJHguZ0bSuV\nSo7d4eLl2o0ICNydfRsSkQSlXMKs1EiO1Q9wvH6AxKwkEmIvbCleEARMkSouz4tFq5bR3D1KRcsw\nFc3DXLE4mcQUPdmzTcwuiMcUr0UZJsPj9mHutdHdbqWhqh+fN0Dh8hSS0yefwYrkclS5udiKi3GU\nHkeRnEJKWj6x6hjKzBUcGygnThVDtCpq0uMBqofqeKf5IzRRcaxd8z3GSk/gOHYUXbwBnyGOrlYr\nTTX98PEbiHrbscyZwY4sLzKxlBm6tDOed7Dfzo73aqg+0UMgGKRgcRIrb5yDbtFCxmqqcVZWYD9y\nGMvHHzH0zmasO7Yxum8vtiNFOEqP46ypxtXUGDKkzGbG+/pxd3bgaqhnrLwMe/FhnPsPMK/Gwaxa\nG86SEhzlZTgb6xlva8XV3ISruYnx1hbGW1sZb2/D3dGBu6sTd3cXnp4e3H09eIeHkRpPz2T9KtDK\nwikdqKTd1sllcYvOq9zON4VLhtXpjLe30/PH3xH0+4l74keEZXw9A52n0ndeX4Cn3q7E4fJR32Fl\nSW7MpHGuXwQhnb23qRyqYY4xl7uybr7g769YJGaOMRd/0E/lUC3HzWWkRSSjU5xZBicQDPBizRvU\nDNcz25DDvTPvQHSOeNyvimAwSIfZzp7Sbgpmxny7DSsAjUyNIAhUDtVi9zqYY8wBYGPDu5idg3w3\n9zYGBkTUtluZV5hJ0pVr8I+O4qyuZPTQAdy9PchMJgy33D7xzyQTyyZSSmcbc0/RGpoq4TINHr+H\n6uF6jtb1M9IXzm0r0lk1L35iH4lOz+i+PXj6+/H09uK32TA9+Mh5VUyfDh+0bqd6uI5VictZZArV\nEkyM1rBuYRLJppDrUySIyNLNoLj/BFVDtcw25KCRnTprUqnkFHWc4EjfMZbELZj47AE0YTJmJuk5\nWmfmWN0AWpWMhCj1BQfbi0QCKaZwLs+LY8Thprbdit3pIW9GyCgWS0RE6MNITNWTOzeOnDwThmgN\nMrkEY7SawstSEInO3AaxWoMiLR370SPYj5WgnJFBQuJMUrRJlJorOD5QjlEZSZzadNqxdo+Dpyv+\nRiDg57G8BzBEJ6GaPQdH2Qkcx4+RmG7AY0ggvGYfkQN1WJQmTiiXYInuomWkA21vPEJAhDJMOlEI\n2uX0ULSrmQM7Ghmzu0nNNLLullmkZBgQiUWIFAo08wtxd3TgHRpArFYjN8WiSElBmZWFevYcNAWF\nhC9dhm7VavTrribyhpvIvPdOJHMKUOXPJSwzG1liEpXBXsbkAsYwA/6RETy9PSHjq6kRV0M9rvo6\nnLU1OGuqcVZXMVZVEZqklJeF7vHE8ZAcRHg4ipSpZWh+kQiCgPvTguoGpZ7E8PhzH/QN5ZJhdSqe\n/j66f/t/CbhcmB76/pc+Ob0QptJ3u090U1I3QIw+DKvdzdDoOIXZU1VXnB47Ovayp+sAiZp4vj/7\nu19YzU1BEMjSzyBCFk75YDUl/aVEhxkxTZLUFQwG2djwDsfMZcyISOXhWfciEV9c7bBgMEjXgINd\nJ7p5ZXsDW4s7aewa5a4rsr79hhVASngS1UN1E643t9/D280fkhKexHVpV2LUKjhc3c+Iw83iOfGo\n8+cii4tnrKaaoNtN5LXXTxQc/gyJSMyJgQqkYgkzIzMJBIM0do1gd3pRyMRIJee2pE2KOPa2H8Mb\nZmZhwmxuXZpzynZBEPCY+xlvasQ/Oop6XgERyy+/4M/jbHTYunitbjMGZSQP5H7nlMDCz89SwqRK\nosOMHDOX0WRtYaGpAMlJ+6tUcl4qe4vhcSv3ZN+G+nOGV4RaTnqclqN1ZkobhzheP0CEWhYqxHyB\nBpZUImJWaiQVzUNUtVqIM6gmxGBP2U8mITJKTUqGIWSMnMWomjgm0oAiKRnb0WIcx48Rlj2TmNh0\nMnTplA1UccJcjlqqPiVjLhgM8nLtRjrtPVyfvo48YyieTBIejjpvbmj1p6KMOLkDZXslokgjktse\nRKlWY7ePY1X1M9DuoLvYS1lxJ21NQwz02jiwswlzjw2dIYw1181k7qJE5IrPuYllMsIXL0F/1dXo\nVq1Gu3QZmvmFqGfPISwzC0VKCvLYWKSRkYjVakRSKWqtCrdIjswYhSIxiVqtiy3SemKWrmTJLY+F\nzrX2CsIXLUE9v5DwhYvRLFiIpnABmoJC1PMKUM+dhzpvLqq8PFSz56DKycXZUI+7owPtipVnDPL/\nMtErdOztOkStpTGURGJpotPezbDLwrjfjVgkRi6Wf+3cC9PlkmH1d7yWYbp/82v8o6NE3fNdtIuW\nXOwmnZVz9d3YuJc/v1uFRCzwH/cV0tI7SnXbmce4C+GEuZxNje+ik0fww/yHp+Sumy6J4fEkhydQ\nPljFMXMZMpGUVG3SKd/BD1q3s6+7iARNHI/lPYBccmH1GC+E3qExdp/o5rWdjXx0uIOm7lF8/iAF\nWVHctCyVtETdP4ZhFXK9JXK4r4RGawu9jn4GXcOsz7qFqDADBq2Chs4Ratut5Kbq0WsUyGNjCV+0\nGHliIuGLl54mAWBQ6jnYc4Q+h5llsYt54eN6Nu1p5kBFL1uLOzhQ0Ut16zBtfXaGR124PX6kEhFy\nqRhBEPD6/Gx4uwZznwiJoReZxs6i2PmnDeiCWIz96BEAou6+90sNtPQH/Pyl8kVsHjsP5N5NtOrc\nAfAxqiicXifVw/WMum2nrEpZfRbeqH6PDF06qxM/L8YfwqBVsjg3hnGPj9p2KyV1A1S1WjBGKDBO\nUmx5OohFApkJERyq7KOyZZiFOdEo5V/MLEcWHY3MFIv9aDH2E8dRzZ6D0ZjITH2o+HbpYCVSQULa\np+UVjvQd55POfcyISOWOzJtO6WexWo16XgFjlZWMt7YgUipJ/Ok/E5OdREqGkXmZWRzsPoJba2NZ\n7CJEgojhAQeD/Q7EEoGFK9K4/KpMIvRf3KB38uAeDAZ5rX4zNo+d7868c2JwFSRSxBoN0kgDUqMR\nWVQ0sugYZCYT8tg45HHxyBMSUCQmoUhKRpGSit/hwFlTjUQfiSI5+Qtr71RRShRIBAlOnwuL20qP\no4+20Q6qh+s42n+CPV0H2dV1gNKBChoszXQ7+ggSxKCM/MrbeiFcMqxC+O12un/7P3gHBzHcdAu6\n1VNR+bm4nKvv3jvQRl2HlRuWpZKbGsmM+AgOVPRS12Fl6WzTGbO0p0vraDvPVr2CTCTlh/kPYwz7\n8r4DxjADOZFZVA/XUT5Yjc3rYKY+A5EgYlfnfj5u20mU0vClGXfnwmx1sresh9d3NvDeoTYaukZw\ne/3MnWHghmWp3HtVFgtmRmOKVH37Y6xOJlyuIRAMUD1cx6BrmKTwBK5PuwpBEBAEAWOEgqKqfqw2\nN4tyQzFOIoUCeXzCpLpKIkGE1T1C40gLFVV+quvdpMaGMzfDiFIuxuHy0jngoK3PRmXLMEXV/ewo\n6WLX8W7Kmgc5UNFHc4+NuUnJJCYK1FmbUEtVJGtPDaaU6PWMHjqAPC6eyOtv/FJn0js79nHcXM5i\nUyErEqee9ZWhS6d2uJ4aSwMGhZ54TUh8clvbLpot7dyYfvWky7ufoZRLyJthpDA7CpvTS227hcPV\n/TR2jRBrUKHTTH92YnN6qG2zIJWISIhWc7xhkPZ+O4tzz1w4e7rIY+OQREbiKDmKo+wE6ry56PQm\nZhlnUjlYS/lQNb6AD4NSzzNVLyEVSXk874FJBwaxMgxNwXwC4+MYb7kNRVLyxDapSII36KPO2sCM\nNBNXL19E3oJEUjIMFCxNJi5JN6WVtulw8uDeOtrB9o7dzDHksDx+0QWdVx6fwMieXbi7O4m4fOU5\nNcu+DNIiUlgSt4A1iZezPH4xc4w5pEWkYFJFf6raHGTQOUTPWB8to22U9JciFSSkapO/MStZlwyr\nUP2/nt//Bnd3F7q1V37p4+cXxdn6bmjExXMf1aLTyHnwmpmIxSLUSikSkUBZ0xAjDs+ktWE9fi9v\nN39ExWA1LSPtdNq76R8bYGjcgs1tx+UbxxfwIRIExIKY4XELT5VNXVbhiyBcrmFu1GwarM3UDNfT\nae/B5XOxpelDtLJwfjT3YSIUk1cJ+bKwOT38YVM5b+1tob7DitPtY066geuWJv8/9s47vK3y7P8f\n7W1Ztrz3imfsJM4OCYQEQiCQhN2Gssou7UsLHfR9OygtnbR0sVr2CCtAwioQErLjOMN7721rWJKt\nPc7vD4MTk2U7DiS/8rkuXYbo6OhIR89zvud+7vt7c9PKHBYUxBFv1IypzPzSnNfPFC5KPZ9yUxU9\nzj4uTl0+ZpBlJxvITTFQ1WqlqctOZuLJT2ChoYhtXbvpCtRTmLGMO9cUoDjibsHtDdBnddFrcdJr\ncX32cNLW60BQD5KdmsRtl+XhDqVSY21gU8sHFEXlj0ngE8tkpPziQURS6WmdFPqcA3zQthm9XMfa\nzEsm9FqZWMrN+dfxu9K/8kr9m6SEJRKu0LOtbS96eRiF4yxtj4vUcNeaAtr6HGzY1kJ1q5UHn9tP\n8bQo1i5JP26YWxAETHYPjZ02GrtsNHbZ6bWMNLyWy8Q8dOt8iqdFcaDBxHt727l0YeqEPt+J0C9a\nTMjjwbT+Jbr+/AeSfvxTYiKi+EHxnfz90L/4qH0ru7pL8AZ93JB3LZGq4yfpS/V6Yq6/8ZjPLU1c\nxJaOHXzSsZ1zExcil8pHfbZOhiAI1FjrkYqkJ22nBCMl3J83zQbY0rlj5BiSFo/r/U6ENDwc/ZJz\nsW35BMfePejPOfV9ThaRSIROrkUn1x5V3SsIAnafg+7hPtbXbWBjywc4fENcnrXqrKuG/G/FsXM7\nntYWdPMWYLzqmrNCVJ2MN7e3EAgKXL4kY0xk6sK5SeyrG2BPdR/z8qIpzBi7srGtaxfbunaN6z3E\nIjFikZhAKDApW4VTwaAM5/uz7uSpqhepttRRbalDLVVx94xbiBxHxfVU4vT4efiVMjoHhslNMbCw\nIJaZWVGolVMvg87aiBWM/GAKjDmkhSVTFFVw1ECLClexs7IXi8PDwoKjk4+PxOrw8NiGJtyqTqQ6\nO/ctX4NGMbZnmkwqxqBTkBStIy81grm5MeTnyDHpd2PXVWKMd7MwYTZKqRKNTE2ZqQqL20rxFxzd\nxQrFae2cHhJC/KvyeSweK9fnXXvMCr+ToZGpiVJHsr+/jCZbKwICleYalicvGdfF/EjCtQoWFsSS\nnRROn9VFddsgWw91Y7F7SI7RoZRL6BwYprRugA/3dbL+k0be3d3OoUYz7f3D+IMhcpINZMSH0d4/\nzJDLx3Urstlb0095k4X8tAgiwqauv50qPQMkEpwHD+CsqEA3Zy4aTTizYgqpszZi8QwyK7qQVWkX\nTnpyl0lk+EI+aqz1aOUa0vTja8ticVt5ruYV3mv9mJK+A3gDXqYZMo4rDtr7hvj9ywfZtL2FJUXx\nOPw2Xql/iyRdApelXzQlFyd5QiK2LZ/g6+76yqJWJ0MkEqGUKolWG5kZPZ1aawOVllpMbjPTjbmn\nLK6CoSC9zn50Mu1pueB/mRGrYCiIyWWmydbCoYEqdvbs5YO2zezo3otKoiROM3VR4vES8vvpffxR\nhFCQhHvuRaI8s/tZHsnxzl1rr4OXNzeSEqPjmxeO7QErFonIjNezvbyHug4bS4riR/N83QEPT1W9\niFQs5b7iu5kXV8yMqALyI3PICk8nNSyJBG0cUaooDEo9GqkauUTOsuQlnD8FN1MTRSaWUhxdxLDf\nhd1r547CG7/0QhO3N8DDr5bT3j/E0pkJ3LE6n+QY3bhyp//rIlYwkrwaoTx21GBaUjj5qQaq2wap\n7xgkO/nY2/VanDz8ahlWh5fc9DzaRKWUW6pYnDD/uO/rDfr4oHUzWzp3EBSCaGUamu2t7OrZx+KE\n+SyIm0NJ70HKzdWUm6ooipqYWeapsLN7L832NmZETR9Nqp4Ms6ILaUhYwI7uPbzV9B4SkfiU2gzk\npBj46beKKWs08+b2FnZW9rK3pg+ZVIzbGxzdTq+RMzs7iqykcKYlhpMYrUEiFhMKCfRYnOyp7uf8\nWYncuiqPP64/xBObqnng5rlTlm8FEHHxKkJuN4P/eZ/uv/yRxPt+QphGxz0z76DMVMms6MJTvrgs\nTTqHrZ072Nz+KYvj55/QLiAYCrKlcwfvt36ML+RnWngGdp+DTzq30+po5+b8dWMio4Ig8OmhbtZ/\n0kggONK2qrzJTLe8FAGBpYnnTNnFURYRiX7RYuzbP2WotISw+QtP/qKvEIMynB/MupPHKp5lf38Z\nwz4nt07/FspJNp9usbezvm4DPc4+cgxZfCPninH5n33VhIQQAy4Tvc4Bep199DkH6HX2M+AyERCC\nY7aVS+QEQ0GeqVnPlq6dXJ65iszP8g2/DBy7dxEYtGK4YMUZ06rmVBAEgde2jHSouHppxjGrpxOj\ntVyyIIVNu9p4fWsT11+UA8CnnTtxBlxcmr6CJN3Je4SeCUjEEq7NXvuV+FR5fUH++no5rb0OFk2P\nZd0XROzp4KyOWI2HaIOaHRW9WOweFk0/OmrV0uPgT6+UYXf6uOLcdC6bk8enXbtwBVwsjJ971PaC\nIHDIVMkTFc9Sba0jXKHn+ryrWZW+gj29pdRZG5kXNwuVVEWaPpldPftosrWyMH4usikqaz0Rgx4b\nT1Y+h1wi566im1GeYrVFtiGTKksdDt8Q85NmMTe6+JT2d9ifKoFog4pusxO5TMqsLCPLi5O45vxM\nLl+SztzcGDLi9YRrFaOTjkgkIt6oYWdlL91mJ2uWpBMMCZQ3WRgc8lCcfXy/qckcpzo3b8RIs6Ic\nd0M9ujnzkCtUJOkSpqREWS6R4w54qLE2EKYII/U4ffpa7PI1LXIAACAASURBVO08XvEMpf2HUElV\nfCPnCi7PXMX8uGIsbis11nr29R0kURtPlDoStzfAU+/V8kFJB2qljCvOy6CqxYo/5KM6tAWNTMW6\n3CundAlMkZCAbesWfN3d6M87/4xfppFJZMyOKaLH2UuNtZ46awNFUQUoJOM3tXX5XbzeuIlXG95i\nyD9MvCaWVkcHu3tKUEgUpIQlTtn3MNURq5AQ4tHyp9nQ9C4HBypotLXQ6+zHF/IRr40j1zCNObEz\nWZp4DpelXsiFomxmiONx6+TUDTayt3c/3cO9JOri0cqmtnLtiwiBAL1P/BMhECDujrsQn0XRKjj2\nuStvsvD+3nYKMyK5dNHxBWpmop6DjSYqW6xkJ4Wj1gg8Xf0SSomCm/K/MWVWCV8WX/a84A8E+fuG\nCuo6bMzNjeaWS/ImnL/6X5W8Pl4iwpS09DioaRtkWlL4mMq06lYrj7xejscX4MaVOVwwOwmVTEWz\nrY1GWwtzYmagOWLS6HcO8Ez1ej7u+JRAyM+FKUv5dsE6ErRxKKVK1FIVZaZKLJ5BimOK0Mq1BD9L\nsPcGfeRH5pzWzyoIAs/WrKfX2c+109aSaTj1O0qJWEKOIWukMWbRaqTBybmpfxGRSERStI5lxYks\nL05kZlYUyTE6NCrZCQdfpF5Jt9lJdauVGIOa84sTqWq1UtliJTpcRVL01DkWi0QiNAXT8ZtNuCor\ncLc0o5szd0ptBRK0cWzv2k3HUDdLEhciOULsuPwu3mjcxGsNbzPkH2Zh3FxuL7yRNP1If0CpWMqM\nqOno5FoqzTWU9B3ENuzltXcHaegcySu879oZFKRFUt5sodVfSUjXx4UpS5lmmNhy7smQqDX4zWZc\ntdWfVRBOfPn5c5weP39aX4bN6SM76fgGg6eKRCxhZlQhdq+DKksd5aYqCoy5qE9SpSQIAvv7y3i8\n4lma7K3EaWK4dfr1rM5YSZTaSP1gE+WmKuqsjaTpU47yg5sMUy2stnXtZlv3btLCUliadA7nJy3m\nsoyLWJ1xMYvi55IbjMDY0I90616GXn0dx6dbCJYeJL3GQn5CIYPhcurszezo3suw30mKLmlM/9ap\nxLFrB0N7dhN+/jJ0s4++2T3T+eK5C4ZC/POtSpweP3dfXkiY+vjf2+c+fjsqemjotOGNqKfB1sQl\n6RdO+Rj+/41AMMSjb1VR2WplRqaR21fnj/oFToSvhdVxiI1Qs728B7PNzaLpcYhEIvbV9vPoW1UI\nwF1rCpiff7hvm0gkotxUhVqqYpohE2/Qx3utH/FczauY3GZyI6ZxZ9FNzIopGuMLlaiLp2GwmVpr\nAwmaWGI1MaSFJXPQVEGtpYG8yGzCFVNbBeEP+ulx9lFnbWRb124ODJSTY8ji8qxVU3e3LFNTHFNE\nXETkGVGZlBarY+uhHpp77Jw/M5GC9Ah2VI5YMMzJjUGjnLr8NZFIhLZoBr7ublxVFXi7OtEVz56y\nPCKFRI7T76LW2kC4Qk9KWCKCIHCgv4zHKp+lydZK7GcX7vOSFiH/wnKhSCQiJSyJ3IhsyvpraXY2\n4JaYODdjBrevKkTzWSufACFqQ1uQSgVuyv/mabkIyuMTsG39BF9vL/pzz5v07++VT5o42GCirn2Q\ntDgdMVNoO/FFxCIx0415hIQQFeYaDgyUk23IOq5J8IDLzDPVL7O5cxsCApemrWBd5hq0/Q68XV2k\npkxnQcJcBj02aqz17O7ZB4hI16ecUoTwZMJKEASEQICQxwOhEEgkx/3+LW4r/6p6AaVEwQ+K7yI3\nYhoRPinBylpsH3+Iaf2LWN9/D1dlBf6+PqQRkehmz0WVlYW3ox1ZVSPTDvWTYEim3yCh1t7Ezu4S\nxCIRybqESTXhPe7nCgTofeIxBJ+P+Du+g1g5fssWlyeASMSUV9hOlC+eu21lPeyq7OPcGfEsLjz5\nUp5Bp8DrD1LR3ku3egc6hYYb866d0u/5/zeCoRBPvlPDoUYz+WkR3H15ATLp5L6v/8ocq/GQFhdG\nUcbIXXtN+yB9Fhcvf9yAQi7he1cUkpMyNvfq8yWBks+WV95ofIdBrw2DIpwrp11GkfHYvfTEIjHr\ncq7goX1/4bWGEb8ntUzFN7Kv4K+HnuDlug38ePb3JjUgBEHA6rHR4+yle7iPnuFeup19DLhMhITQ\n6HZamYZv5Fx+xi/FnArGcBUXzUvi3d3tvL+3nbVL0vnWhdP497u1PLmpmp+smzWljUxFEgmxt91B\nzz/+irO8jL6n/0XsLbdPmbhannwuO7r38FH7VrLC03mjcRO11gZkYimXpV/EsuQlJwz5e31BPt4+\nhLluDqqsKiT6fmrkG2kb0o7mwYQn2hFb3Wg8mRNqkDoR5DEx6ObNZ2jvHobLDqGbNfFl45YeB9sO\ndWPUK7ENe3nqvVp+dfNc9NrTZyAoEom4NOMidAodbzRs4pGDj3F74Q1jIgL+UIDN7Z/yn7YtBIQA\nWRhZ0RmGetsW2rpfGBEzgGbGTOJuv4ubC9ZRbJrBq/Vv8W7rhxwyVbAu58oxJrPjIeTxYHnnbcz2\nQTxDTgSfj5DP99lfL4LP/9n/e0EQDr9QLEasUiFRqRGrVCMPtRqRSskr8f34FD4uDWTgfnMjlpoa\nfD3doy+VaHXo5sxFnZePOjdvtPUXgHHtlQyVlmDb8glJ22v5hhhqZkazZ1qAt5vfZ1vXblZnrKQ4\npmhKlpodJXvwm03oly5DGj6+NlltfQ4+Lu1kX+0AidFafnjtzNNS+TUZ3N4AG3e0oJBJWHPO+FcU\n1pyTxh7rFnyiAMXhC05bdHCi7K8boMfi5OL5KV9a8+iTERIEnnm/jv11A0xLCufuy6dPWlRNlv+K\niBWMlP5vKxsx+dxfbyJMLeO+a2ce04ZBKpYw4DLTaGvh4EAF/s+W/W4uWEeiNu6EomXkoiWi0lKD\nO+BmujGPSFUEVs8gtdYGFBIFGePwEAmEAjQOtrC9azfvt21mQ+O7fNwx4kvVMNhEr7OfQChAki6B\nfGMuC+LmcGHKUlZnXESY4vQkd55JXjppcWHsrOyltn2QBfkxTEsKZ2DQTWWLFQHITTm1XoVfRCSR\noJ1VjLuxAWdlBYLPhyZ/agoSlFIFQ75haq0N7Ojeg8ltGYmKFt5MYVT+CS9Q3eaRwova9kHSYsO5\nb8XFhGvUVFpqKOk7gEwsJV2fwpstGzG7rAzV5XF+YfqUmA4KoRC+3l4QBERyOSKRCEVcPLZPt+Dv\n70O/ZGJRq1BI4O8bKrE5fXz3iukkRmk50GCiy+Rkfv6pNyI/GalhycSoozk4UEFp3yGiVUYiB/1U\nlm/hyfpXKHM0oHQHWF7iYN7OPsTtXYQ8HpQpqWhnFSOSyXBVV+Fpa0U7q5i4sDgWxM3BFXBRbaln\nd08p3qCPDH3auG6uAnY7XX/5E8MH9uPu6sZvGiBgtRByuyAYRCSRIFGpkOr1yKOjkcfFIU9IQB4V\njUSnQySRIPh8BO02/P39+Pv6KJOZ2Z8qIqXHy5z36vG2tBDyelHn5KI/bynGK68m6qpr0M2ZizIl\nFYl6rAgXSaUok1PQLzkPzfQiRIEAhoNNFNQ6EKQS2vVBDpkrqbHWU2jMn1DO2hcRgkF6n3iMkMdD\n/B13IVEdP3IZDIU4WG/iuf/U8db2VrpMTjRKGSabm+ZuO/PyopF8RdWqR86b7+xuo7LFyqqFqRQd\np4fpsRgODPOJ+V2CXjm2mhyWFCUi+QojcaGQwOtbm1n/SSN1HSO2OEWZxikzM50sgiDw4kcN7Kjo\nJT0+jO9fXXTKPRcnE7ESCUfe5Zx+BJNp6Mt8vzH8fUMFhxrNGPVK7r12BjGG4w/UNkcHfz7wGNMM\nGVw1bTUx6pO7ln9OIBTg96V/o8fZxz0z7yDLkM6w38mDe/+EN+jj/+bde8yqIbt3aNTro87agCfo\nBUYiYdEqIwnaOOK1sSN/NbFEKA1famQqKkrHV3n+vsiuyl6eeq+WubnR3LG6AJcnwC+f2YfF7uFH\n35x53CrQUyHoctLxmwfx9/cRd8d30M2eMyX7tXnt/GrvH1FIFFyZdSmzootOem53V/Xy/If1+Pwh\nlhcncvX5maN3jU22Vp6uehG7b4gMfRrN9laixMl07M3jxpU5LCk6tWoiZ3UV5g2v4+1oB0AklyOL\nNCKNNOIf6MM/MIBh5cXoZs1GajQi0epO+nk+OdDFSx83sLAglltW5SEIAn99o4KKZgvXnJ/JirnJ\nJ3z9qSIEAnja26hq3MvLkkp84hDJfT464hQgCBQ2eVlqjUCfmIYyLQ1lShry+PjRnLuQ30fvY//E\nWVGOKmsa8d/7PhLVyNJVvbWJl+vewOyxEqWKZF3OlSdswu3r76P7kYfxm0yELVpMzu03YR0OIJKd\nOAfxuJ8tFMJmN/FQxT8JhoLcG70WvV+CWKVCmZ5xSvYvweFh7Du3Y/90Kxa3hR0ztTQlK5kXUcj1\nM66b9H4de3bT99ST6M9dSsy3bjjmNi6Pn+3lvXxyoAuLwwPA9PRILpiTSG6KgcffruZAg4mZWUbu\nWlvwlYirz+fNwSEv9z+xB5VSyu9uW4BCPn4R8lrDRrZ17SI9uIjqAzpWLUzh8iXH//2cTtzeAE9s\nqqai2UJshJrYCDVlTWZiItTcc2XhaV26PxGCIPDqliY+Ku0kOVrLD785c0rSQqKidBMecP9Vwmpw\nyMvWQ12cPyuR8HEsLfiDfqTiyRl5ttrbefjAo0Srjdw/5x5kEhn7+g7yXM0r5EVkc1fRzQgItDu6\nPhNTtXQMHQ7HG5UR5BtzKYjMITM8/ajcmq+CM01YhQSB3zx/gNZeBz9ZN4tpSeE0ddv53YsH0Wvl\n/Orbc6c03+pzvN3ddDz0K0BE8v/+HEX81JQ8271DKKWKk97lDw55eePTZvZU96FSSLhpZS6zc46u\niBzyDfNM9cvUD46UdX8r81s8+bKJvFQD9107c1LH6GlrxbzhdVy1NcDI0pdIJMZvMeM3mwm5n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WUomYMI2c0joTgw4vc3NHJqLMRD0d/cNUtVoJCQK5KVPfHFcWEYFEp2N4fynuxgbCFi6ccNub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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c48b110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(paths[:, :10]).plot(legend=False, figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3971083435069782"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call_estimator(v0, kappa, theta, sigma, T, r, K, M, I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.62 s, sys: 136 ms, total: 6.76 s\n",
      "Wall time: 6.81 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "estimates = []\n",
    "for k in strikes:\n",
    "    estimates.append(call_estimator(v0, kappa, theta, sigma,\n",
    "                                    T, r, k, M, I))\n",
    "data['estimates'] = estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5IR6giIiEHYUpqVlKLW4vWL6U2UuieO65fGrVCvDUU7G0b+/inXdQfyoREakw\nhSmpeUotbo+O/nM91Z13FpKdbeOaa6BHDydr10aHeqQiIhIGFKZECN40eP/9RaxcmcvAgfD559H0\n6uVk2PVe/m/BBt35JyIiZVKYEinlpJMCvP8+zJ+fS4ezfuM/89vT/Lqu+Fp3IecnBSoREfk7hSmR\nA0hO9vPhYxtpXnLnX/1sw00dv2XSJAfFxSEenIiIVCsKUyJl8Cf9eeff7mMtvvCdwf33x9G5s5O0\ntGgCgRAPUEREqgWFKZGylLrzz7Z+KZ+ss3HddUV8800UV1/tpH//eL78Uj9CIiI1nX4TiBxMqTv/\n6tYN8OSThaSn53HBBV5WrLBz/vlOhg0rafrp8WDfuF6L1UVEahiFKZFDZFl+Jk/OZ/r0PJo39zN1\nqoPz2wXwt+lCQo9uJKSmKFCJiNQg9ooeaFlWHLAWSDPG3LPfa7HAU8CPQFPgCWNMVlUOVKS66dzZ\nx5IleUybZmfhw1s4/lcDgD0rE9tXGQTOSQ7xCEVE5Gg4lJmpR4HPynhtOPCdMeZx4FlgUmUHJhIO\n/mj6OXH5yfz8DwuADJK4YHhrLVIXEakhKhSmLMsaBKwAvi3jkJ7AagBjzBagpWVZ7qoYoEg4cNZz\nE7VhKV9P/oSnB67kix3HcPXVTvr2jefzz3U1XUQkkpX7Lm9ZVnMgyRjz4UEOqwfklHqcU/KcSM3h\ndnPMBW14bIKDZcvySE31smqVndRUF0OHxvHtt7ZQj1BERI4AW6Cc6xCWZT1AMHQVA+cDDmCmMWZC\nqWOWAQ8YY1aWPP4NaGCMKXMVrtfrC9jt2vtMIlt6Ovz737BhAzgccOutMGIEHHtsqEcmIiKHqMx/\nEZcbpkqzLOs/gMsYc49lWQmA1xiTY1nWPYDfGPOUZVktgP8aYzof7Fx79uRE9GqSunVrsWdPTvkH\nSrVT1bXz+2H2bDtjxsSyc2cUxxwTYPjwQoYMKSYurso+jZTQz154U/3CV6TXrm7dWmWGqQov5rAs\nqx/QCWhnWdblwH3AzSUvTwAaW5b1IHAnMPjwhysSWaKioG9fLytX5vLwwwVERcEjj8TRvr2LadPs\n+HyhHqGIiFTGIc1MVSXNTEl1daRrt3cvPPdcLK+95qCw0MZpp/l46KFCunTxYdOyqkrTz154U/3C\nV6TXrkpmpkSkatSpA//5TyFrFv/MA91WsvOrPAYODG5Pozv/RETCj965RULB4+GMf3ZmzJIO7GrU\nhotT9rKrOYEZAAAgAElEQVRiRfDOvyFD4tixQ1NUIiLhQmFKJATsJgN7ViYArp2ZvH3vZ8yalcfZ\nZ/uYM8dBhw4u7r03lt27FapERKo7hSmREPBazfEmNgv+ObEZXqs5553nY+HCPCZNyqdRowBvvBHD\nOee4eOKJGG31JyJSjSlMiYSC2012WjrZC5eQnZYO7uCGATYb9Orl5dNPc3nyyQJcrgDjx8dyzjku\nXnvNQVFRaIctIiJ/pzAlEipuN97WyfuCVGkOB1x3XTFr1+Zy772FROXl8uEDmzm/XYDp09VOQUSk\nOlGYEqnG3G64+8Zf2Vm/DWtpx6wf2nLPrT66dnVqI2URkWpCYUqkmrObDGJ3BBerJ7GN4Rdsxpgo\nrr7aycUXO1mzRtsyiYiEksKUSDW3/2L1O15uQnp6Hj16FLN+fTSXXOLkyivj+fJL/TiLiISC3n1F\nqrsDLFZPSvLz1lsFLFiQy3nneVm82E63bi5uukk9qkREjjaFKZFwUMZi9TZt/Mycmc/UqXm0bOlj\n5kwHqecFmHj9ZnbvyA3RYEVEahaFKZEwZ7NBly4+Pvoojzee38OGqHMYOb8j0ed2YeyDxfz6a6hH\nKCIS2RSmRCJEVBT0bvolTYu3AZAU2MbKV7No08bNuHEx5ETu/qMiIiGlMCUSQUovVi9u2oz+I08l\nLi7AuHGxJCe7eOEFB/n5IR6kiEiEUZgSiSSlFqvv/Sid64fFsm5dLg88UIjXa+Phh+No29bFG2+o\nm7qISFVRmBKJNPstVne7YfjwIjZs8DB8eCG//27j3nvjaN/exdSpdvy/e7BvXI82ABQROTwKUyI1\nRJ068MADRaxbl8sNNxSxa5eN+4b58JzWlYQe3UhITVGgEhE5DApTIjVMvXoBxowpZM2aXO5O3cSp\nRcEF6/asTDa8laktakREDpHClEgN1bBhgNteOpX8xsEF6xkkMeDh1vTo4WTp0ira98+jS4giEvkU\npkRqMrcbz9LggvVfF6XTtVcsn30WzeWXO7nkknhWrqzEvn8eDwmpKbqEKCIRT2FKpKYrWbDe7Gwn\nkyYVsGRJLqmpXtautdO3r5P+/eNZt67kreIQZprsJgN7VnCDZntWJnaTcSS/ChGRkFGYEpG/aNHC\nzzvv5LNoUS5dunj59FM7F1/s4voBPpydKz7TtP8GzV6r+VEYvYjI0acwJSIHdPbZfqZOzWfOnDzO\nO8/LnmUG1/eHMNN0gA2aRUQikcKUiBxUu3Y+Zs3K54F3T+GbuCQguFj9hmdbsXVrOW8hZWzQLCIS\nSRSmRKRC2nePx/3VJ3w8Op2hZ61hRloCXbq4GDw4jowMvZWISM2ld0ARqTi3m7NuPJsZaVG8/34e\nrVr5mDvXQUqKkxtuiGPbNr2liEjNo3c+ETlkNht06+Zj0aI8Jk/Oo2VLP7NnO+jc2cnQoXFkZuqt\nRURqDr3jichhs9ngggt8fPRRHu+8k8cZZ/j58EMHHTs6uemmOLKy9BYjIpFP73QiUmk2G6Sm+li8\nOI+33srn9NP9zJz5Z6jSTJWIRDJ7eQdYlmUD5gJrgFjgVOB6Y0xhqWOuBW4C8kuemmSMmVz1wxWR\n6sxmgx49vKSmelm40M64cTHMnOlg1iw7ffp4ufPOIpKS/KEepohIlSo3TJVYZYx5DMCyrA+BfsD7\n+x1zuTFmZ1UOTkTCU1QU9OzppUcPL4sW2Rk/PoZZsxx8+KGdXr283HVXEaedplAlIpGh3DBljAkA\nfwQpO9AAMAc4dJhlWbsAJ/BfY0x2VQ5URMJPVBRcdFEwVKWlRTN+fCxz5jiYM8dBz57F3H13EWec\noVAlIuGtwgsZLMvqTvBy3zxjzGf7vZwOPG6MGQ9sAKZX2QhFJOzZbHDhhcGF6pMnB1sqzJ/voGtX\nF9deG8cXX2hNlYiEL1sgEDikD7As6y1gtTFmYhmvxwIeIKZkVuuAvF5fwG6vxI70IhK2AgFIS4OH\nH4Y1a4LP9eoFDz0EbdqEdmwiImWwlflCeWHKsqzmwCnGmAUlj0cRvJQ3FvAaY3Isy3oMGGmM8VmW\n1QKYYYxpdrDz7tmTc2gpLszUrVuLPXtyQj0MOQyq3dETCEB6ejRPPRXL+vXBf1x17erlrrsKOeec\nw7v8p/qFN9UvfEV67erWrVVmmKrIAvRC4J+WZZ0FxABJwO3AfcAvwJPALuBFy7K+Bc4ABlVuyCJS\nE9hs0KWLj5SUPFZ9lM/8J7N4/5MzufgTNx06BO/+69DBh23/tzCPB7vJwGs1175/IhJyh3yZr6po\nZkqqK9UuBDweElJTsGdlktPQ4rKT17BoRR0AkpN93HVXIV27loSqUsd6E5uRnZb+l0Cl+oU31S98\nRXrtDjYzpVWfIhJydpOBPSsTgFo/GN578DMWLcolNdXL+vXRXHGFk+7dnSxYYCdq25/H2rMysZuM\nUA5dRERhSkRCz2s1x5sYXGbpTWyG12rO2Wf7eeedfJYsyaVXr2K++CKK666LJ/XO1uytb/3lWBGR\nUKpo004RkSPH7SY7Lf2A66BatPAzaVIBxkTx7LMxzJp1DA39G7iw4RecPzSRS2LjcIRw6CIimpkS\nkerB7cbbOrnMBeWW5eellwpYtSqX3lfGMHvXudz877q0beti0iQH+fkH/DARkSNOYUpEwkqTJgGe\nfbaQdetyGTKkiP/7Pxv33x9HmzYunn8+ht9/D/UIRaSmUZgSkbDUsGGAxx4rZMOGXG6/vZD8fBuj\nR8fSuDE8/ngMv/4a6hGKSE2hMCUiYa1evQAjRhTx+ece7r+/kOhoePrpWM4+281DD8Wya5ct2Jdq\n43rweEI9XBGJQApTIhIRateGO+8s4rvvYPToAmrXDjBxYgydWwcobtWFhB7dSEhNKT9QKXiJyCFS\nmBKRiOJywY03FrNuXS5PP11A5+O2cOJvBgj2pdq50JT9wSUNQSscvEREUJgSkQgVGwuDBhXz0vKT\nyS7pS5VBEp1vbcMVV8SzenU0+28AUbp5qBqCikhFKUyJSESLru3Gu2opvy5YwtY3ltHy3DiWLLHT\nu7eTnj2dLFoUjb9kT+UDNQ8VESmP9uY7QiJ9j6JIptqFt4rUb926KP773xgWLQq2+0xK8nHbbUX0\n7evFUahNlENJP3/hK9Jrp735RERKOeccP2+/XcDy5bkMGFBMVlYUt90WT9u2Ll6bksDvzctuHioi\nsj+FKRGpsZKS/LzwQsG+BqC//GLjgQfiaN3axbhxMfzyS5n/EBUR2UdhSkRqvJNOCjYA3bgxl7vu\nKsTnszFuXCxnn+3i/vtj+e47hSoRKZvClIhIieOOC3DffUV89pmHRx8t4NhjA0yaFEPbti5uvDGO\nL76o5FumeliJRCSFKRGR/bjdMHRoMWvX5vLii/kkJfmZNcvB+ee7uPTSeNLT/95WoVzqYSUSsRSm\nRETK4HDApZd6Wbo0jylT8ujY0cvy5XYuu8xJt25OZs604/VW7FzqYSUSuRSmRETKYbNB164+ZszI\n56OPcundu5ivvorippuCdwC+8oqj3Ikm9bASiVzqM3WERHq/jUim2oW3o1W/b7+1MXFiDO+956Cg\nwEbt2gGuvbaIIUOKqV+/jLc3j3pYlUc/f+Er0munPlMiIlXs5JMDPP54IZ99lss99xTicASYMCGW\n1q1d3H57HBkZB3h7dbvxtlYPK5FIozAlIlIJxx0X4F//KmLjxlzGjy+gcWM/U6Y46NzZxcCB8Sxb\ndhiL1UUkrChMiYhUgfh4uPrqYlasyOOdd/I491wvn3xiZ8AAJ127Opk+3U5xcahHKSJHgsKUiEgV\nioqC1FQfs2fns2hRcLF6RkYUt94aT3Kyi//+18Fvv4V6lCJSlRSmRESOkLPP9vPqqwWsXZvL0KFF\n7N1r45FH4jjzTDcPPhjLt9+qs7pIJFCYEhGpjAp0NW/cOMCjjxayaZOHkSMLqV07wKuvBjurX399\nHGvWaF2VSDhTmBIROVyH2NW8Th0YNqyIDRtyeemlfFq29DN/voNLLnFy4YVOZs3SuiqRcKQwJSJy\nmA63q7nDAf37e/noozzmzMmjR49iNm2K4sYb4znnHK2rEgk3ClMiIoepsl3NbTZo187HW28VsHp1\nLkOGFJGd/dd1VTt2aF2VSHVXbgd0y7JswFxgDRALnApcb4wpLHVMLPAU8CPQFHjCGJN1sPOqA7pU\nV6pdeDvq9aviruZ798I778Tw2msOfvopCpstQPfuPoYOLaJDBx+2CM9W+vkLX5Feu6rogL7KGPOo\nMWYk4AT67ff6cOA7Y8zjwLPApMMaqYhIuKnirual11W9/HI+Z5/tJy3NTv/+TlJSnEye7CA/v0o+\nlYhUkXLDlDEmYIx5DMCyLDvQADD7HdYTWF1y/BagpWVZ2i9BROQwOQo9DGi0moXTd7NgQS59+xaT\nmRnFnXfGcfbZLsaOjWHXrgifphIJExVeM2VZVneCl/vmGWM+2+/lekDpub2ckudERORQ7XeXYJuk\n33n55QI2bszljjsK8fttPPNMLGef7eLmm+P4/POoCrVoEJEjo9w1U/uzLOstYLUxZmKp55YBDxhj\nVpY8/g1oYIwp86fa6/UF7Pbowxu1iEgkW7sW2rX78/GaNdC27b6HeXnw7rvw7LOQkQEuPGyJT+bk\n/G0ErCRsG9ZrM2WRqlfmVLC9vI+0LKs5cIoxZkHJU98ATSzLSgC8xpgcYD5wLrDSsqwWwKaDBSmA\n7Oy8ig4+LEX6QrxIptqFt4ioX71GJCQ2w56ViTexGdn1GsF+X1PfvtCnDyxbFs3ycZs5ef02AGxm\nGy/e+jld7mvFcceF330+EVG/GirSa1e3bq0yXys3TAGFwD8tyzoLiAGSgNuB+4BfgCeBCcA4y7Ie\nJHi33+BKjllEpOZyu8lOSy/3LkGbDVJSfKS0aUJ+l2bEf5eJsSVxz9utKJ7iom9fL0OGFHHmmf6/\nfmAV34EoUtMd8mW+qqLWCFJdqXbhrcbWryQg7W3QnCnzEnjttRh27Agui01O9nHDDUX07OnFURhc\nj7Vv1istvVoFqhpbvwgQ6bWritYIIiJSnZW0aHDXdzNkSDGrVuUyZUoe3bp5Wb8+mqFD42nd2sX0\nUdsPq2u7iJRNYUpEJAJFRUHXrj7efz+f1as93HBDER6PjXvebsU2kgDwnHToXdtF5O8UpkREItyp\npwYYM6aQzZs9jBhr57JT1tKWNdT/fiOp/esxdaqdgoJQj1IkfClMiYjUELVqweDBxXy82sbdU1vQ\nITWOTZuiGDYsnlatXDz6aAzff69GoCKHSmFKRKSGiYqCLl18vPNOPuvW5XLbbcFGoBMmxJKc7OKa\na+JYtiyaEN2fJBJ2FKZERGqwxo0DPPRQEZs2eZgwIZ8WLfwsWuRgwAAnHTo4ee01BzmRe4OWSJVQ\nmBIREeLjYeBALx99lMfChbkMGFDMd99F8cADcbRs6ebf/45l69YK/MrQtjZSAylMiYjIPjYbtG7t\n54UXCvj881wefLCQOnUCvPVWDF26uLj44nhmzLBTWHiAD95vT0EFKqkpFKZEROSA6tYNcMcdRWzY\nkMvbb+fRtauXdevs3HzznwvWd+78c8G63WSoh5XUSApTIiJyUNHRcOGFPqZMyWftWg+33lqEz/fn\ngvWrrorn44+jKUpsjjexGQDeRPWwkppD28kcIZHeVj+SqXbhTfU7OvLzYc4cO2++GcPGjdEANGrk\nZ8jAbAa1+gJ326TD2qZG9QtfkV47bScjIiKHp4wF5fHxcPnlXhYuzGPJklyuvrqI//s/Gw89eSzW\nNSncePdxrFql9gpSMyhMiYjIgVVwQXmLFn7Gjw92WB8zpoAmTfzMmuWgT59ge4WXX3aQnX10hy5y\nNClMiYjIAR3qgvLateGGG4pZvjyPOXPy6Ncv2F5h5Mg4zjzTzW23xbF+fZRmqyTiKEyJiMgBea3D\nW1Bus0G7dj4mTixg8+Zc/vOfAk44IcC0aQ569nTRpYuT11934NmlnlQSGbQA/QiJ9IV4kUy1C2+q\nXxXzeLCbjGCQOowF5X/w+2HFimjeesvBwoV2Yr25bLAlkxTYhuekZuQvSwe3++D1q6KxyJER6T97\nWoAuIiKHx+3G2zq50uElKgo6dfIxaVKwGei4az8nKbAt+Cm+z+SO87/hjTcc/PZbGSdQQ1CpxhSm\nRETkqDr++ACX/qcp3qbBS4jfuyzmf9uCe++N48QT4Y474tiw4a9rq9QQVKozhSkRETn63G6yP0on\ne+ES4r5cysrNNh54oJDjj4f333dw0UUuUlKCGy3v3Xv467dEjgatmTpCIv3acSRT7cKb6hfejj22\nFjNm5PHuuw4WLLDj9dqIiwtwySVeru2fTbtaW/Alac1UdRTpP3sHWzNlP5oDEREROZioKEhJ8ZGS\n4mP3bhtTpzp4910H06Y5mDatHomJnbnqqmIGDPBSt25E/5tcwogu84mISLVUr16AYcOKWL06l5kz\n8+jbN9i3atSoOM4808U//xnHJ59E4/OFeqRS02lmSkREqrWoKOjQwUeHDj5+/RVmzAjOVs2bF/yv\nQQM/AwcWc8UVxTRqpNkqOfo0MyUiImHjH/8IdllPT88jLS24J+Bvv9kYPz6W5GQXl10Wz+zZdgoL\nQz1SqUk0MyUiImHHZoNWrfy0alXII48UMneunXffdZCebic93c4//uFnwAAvV1xRzGmn+UM9XIlw\nmpkSEZGw5nLBwIFe5s3LZ8WKXG65pYioKHj55RhSUlx07+48eENQkUpSmBIRkYjRrJmfUaMK2bQp\nlzfeyKd7dy9ffBHFvffG0aKFm5tuimP58mj8pSerPNojUCpHYUpERCJOTJGH3vVX8+7EPWzalMuI\nEYU0aBBg5kwHl17qJDnZxbhxMfywLVfb1EilKUyJiEhk2W8fv/ruHG6/vYhVq3KZNy+Xq64q4tdf\nbYwbF8tNnb7TNjVSaQpTIiISUcrax89mg3PO8fPMM4V8+aWHCRPycSVbZJAEgIlK4p63zmL9+r/u\nCyhSnnLv5rMsqwnwKLAROAn4xRgzer9jrgVuAvJLnppkjJlcxWMVEREp1x/7+NmzMsvcx8/tDi5a\nHzgwmm+3LOWZl7N4cdmZbJ9yDK9NgSZNgr2rBgwopkEDJSs5uHL35rMsqw1wgjFmbsnjrcAgY8zn\npY65FlhqjNlZ0U+svfmkulLtwpvqF96qrH4eD3aTEQxSFdzHz+eDTz+NZsqU4L6ABQU2bLYAnTr5\nGDiwmB49vDidR34c4SrSf/YqtTefMWbDfk/ZgNwDHDrMsqxdgBP4rzEm+5BGKSIiUlXcbrytkw/p\nQ6Kj/9wX8PffYfZsB1OmOFi2zM6yZXZq1QrQp08xl19eTHKyH1uZv1pLlKzd+mOGLDstPeIDVU1V\n7sxUaZZl9QE6G2Pu3O/5xoDHGPOLZVk9gLuNMecf7Fxery9gt0cfzphFRESOmsxMeOstePtt+OEH\ncOHhwoZbaXPt6Qwc4ubkk8v4wLVroV27Px+vWQNt2x6NIcuRUWZ8rnCYsiwrBehjjBleznGxgAeI\nMcaUeXJd5pPqSrULb6pfeKvO9fP5YPXH+STf2omGOYYMkkhmPS3PjeOyy7z06lXMMceU+oAaNjNV\nnWtXFSp1mQ/AsqyeQAdjzHDLsk4AGgMG8BpjcizLegwYaYzxAc2Abw4WpERERMJNdDSk1N1CQo4B\noDnbuLLlZl5dfR6rV9u5//5YLrrIy2WXFdOpkw+72012WnqNWTNVk1Xkbr6zgSnAesuylhJcE/UC\n0Bf4BXgS2AW8aFnWt8AZwKAjNF4REZGQ2f9OwTEfNuGmbA8ffOBg2jQHM2cG/6tXz0///l4uuyyK\n0w9x7ZaEn0NaM1WVdJlPqivVLrypfuEtLOpXxh16gQB89lkU06Y5mDXLwd69watCp53mY8CAYvr3\n91K/fuT+6guL2lXCwS7zKUwdIZH+TRXJVLvwpvqFt0ipX2EhLF5sZ+pUO0uW2CkuDrZZ6NjRx6WX\nFnPxxd6Iu+oXKbUri8JUCET6N1UkU+3Cm+oX3iKxfr/+Gmyz8MEHDtavD97FHh8foEcPL5deWkzn\nzj4cjhAPsgpEYu1KU5gKgUj/popkql14U/3CW6TX75tvbMyYEQxWO3YEd3Q77jg/ffsGg9VZZ1Wg\nf1U1Fem1O1iY0t58IiIiR8kppwT417+KWL06l4ULcxk8uIhAAF59NYbUVBcXnOtn6l2b+HbLgXpj\nS3WlMCUiInKU2WzQurWfsWML+eKLXN59N4+BF2cz9Zt23PZuJ9xdu9D3Aj+vvupg9+4wnaqqQRSm\nREREQsjhgO7dfbx46+ckBbYBwR5Wvi8yePDBOFq2dHHZZfFMnWonJ3KvooU1hSkREZFq4I8eVgDe\nxGa8vvZkxowpoFUrP+npdoYNi+f0093ccEMcCxfaKc72YN+4HjyeEI9ctAD9CIn0hXiRTLULb6pf\neKvx9Sujh9WOHTZmzXIwY4ad7dujceFhY1Qyln8bvzewyF+2lKhjQttrIdJrpwXoIiIi4cDtxts6\n+W9bzzRpEuDuu4tYuTKPxYtzGdl3E5Y/eEnwmB8N153zLSNGxLJhQxQhmiOp0RSmREREwoTNBi1b\n+vnn+FPxNg1eEvzfMRZb/GfwyisxXHSRi+RkF2PGxLB1q4LV0aIwJSIiEm7cbrI/Sid74RIcm5ay\nZgtMnpzHpZcW88svNp57LpYuXVx06uTk6adj2LFDdwQeSVozdYRE+rXjSKbahTfVL7ypfpWXlxfc\nymbmzOBWNoWFwSB11lk+evcupndvLw0blvMruIy1WwcT6bVTB/QQiPRvqkim2oU31S+8qX5V6/ff\nYcECO7NmOVi+PBqfL5gHkpN99OlTTK9eB9h82eMhITUFe1Ym3sRmZKelVyhQRXrttABdRESkBjrm\nGBg40MvUqfl8+WUu48YV0KGDl40bo3jwwTjOPNNFnz7xvP66gz17glnBbjKwZ2UG/5yVid1khPJL\nCAsKUyIiIpHO46H+d+u4tn82M2fms3lzLmPHFnDOOT5WrbJz331xtGjhon//eN79vCWFTf7sd+W1\nmod48NWfPdQDEBERkSPoAJftjj/ezeDBxQweXMz//mdjzhw7s2c7+PRTO59+ehwjozdwVesvOP2y\nZpxfHEdCqL+Gak5rpo6QSL92HMlUu/Cm+oU31a/q2TeuJ6FHt32PsxcuCfayOoCdO23Mnu1g9mw7\nX3wRHfx4e4BOnXxcckkxPXp4SSgjWUV67bRmSkREpIbaf5uag122a9QowLBhRSxenMfatR5GjCjk\n9NP9fPKJneHDg9vZDBwYz3vv2cnOLvkgj7a10czUERLpCT2SqXbhTfULb6rfEXIYrQ5K+/ZbG3Pm\nOJg7187mzX/OWF1w7m+8a9rxj90GkpLYs+CTwzp/ONDMlIiISE1WxjY1FXXyyQFuv72Ijz/OY906\nDyNHBmesfvnUBIMUwLZtfPRM1r67AssUgTNZmpk6QvSvq/Cl2oU31S+8qX7hZedXuTTqn0LdXwwZ\nJJHMevKjXJx7ro+LL/bSs+d+fawOs4dVdaCZKREREalyjU5zwfqlZC9cgmvreu59xE7r1n5WrrRz\n//3BPlYXXxzPxIkOfvjBFrE9rDQzdYToX1fhS7ULb6pfeFP9wlfp2v30k40FC+zMnWtnzZpo/P7g\npE77lr/x4Y9tqfuLiaiZKfWZEhERkSp1wgmBfX2sdu+2sXChnXnz7KxYcQyn+DZwOlvxRyeR8mIc\nPXt6Oe00P7Yw3otZM1NHiP51Fb5Uu/Cm+oU31S98VaR2v/4KaWl25s93kJ4eTVFRMEGdfLKfnj29\n9OxZzNln+4mqhouQtNFxCOgNIXypduFN9Qtvql/4OtTaeTyweLGd+fPtfPyxnby8YFapX9/PRRcF\nF6+fe64PezW5hqYwFQJ6Qwhfql14U/3Cm+oXvipTu4ICWLYsmvnzHaSl2cnODuaWkxJ+55o2X3L6\ngETOS40nPr4qR3xotGZKREREqq24OEhN9ZGa6sPrhdWro1n8YQHD3zuPZh9vI+PjJNrEryO5Sxw9\nenjp3v0A29pUsjFpZShMiYiISLVht0PHjj66ODeR8M42AJqzjU7HfsmMBe1ZsMBBdHSA9u199Ojh\n5cILvTSskxPS/lXlhinLspoAjwIbgZOAX4wxo/c7JhZ4CvgRaAo8YYzJqvrhioiISE3wx56CfwSk\nl9JO4a6fclm40M7ChXY+/TT43wMPwFVNv+Dd7X/tX1XWZs5HQkVmpv4BvG+MmQtgWdZWy7LmGWM+\nL3XMcOA7Y8xTlmWdAUwCOlX9cEVERKRGcLvJTkv/y6W7xEQ/iYlF3H57ET/9ZGPRomCwmvdpCzJI\nojnb2G5P4vnpZ9GlIJq2bY/OAvZDXoBuWdZXQB9jTGap55YD9xtjVpY83gs0NMaUufGOFqBLdaXa\nhTfVL7ypfuErlLX77TdYNr+AbTMyeeezlvycWwuAOnUCnH++lx49vHTp4q3Ulb8qW4BuWVYfIK10\nkCpRDyj9N5hT8lzk7GIoIiIi1VLt2nDJlXFccmVL7iiElSv/v717j5GrLOM4/l26LaUplwWpNIpF\nqjwgIElBhJSyJKILKQlEE0mMFIyJQhMFJRYswStUoLaAcvESjZAYCFakSNEFiduISm0JwYTLA1S0\njdIqiMBCvWw7/nHOYltnZhfOdmeG/X6SSWfPOXvyTJ5N85tz3vO+r9Df301/fzcrVkxmxYrJTJlS\nY968rfT1FeOsdlgzsKJRX5mKiJMorkhdUGffamDxdlemXgDe0uzK1NDQ1lp396TXVbQkSdJIajV4\n6CFYubJ4Pfzw//adOGeQBUc/wnvOOZwjj58+mhnYq80zFRHzgRMy8/MRMROYBSQwlJkvRcQiYFs5\nZupI4LrM7G12Tm/zqV3Zu85m/zqb/etcndC7jRu76O/vZuCuf3LNb47nMB7nMQ7l9JlrmNs3lb6+\nIRhGOpkAAAdsSURBVObO3crUqf//u5Um7YyIOcBqYC1FKpsGXA8cTvFk31URMRVYCmwCZgNLMvOp\nZuc1TKld2bvOZv86m/3rXJ3Uu+4H19Jz6vte/fnk6b/lvsHjAJg2rUZv7xB9fUOcfPJWZswo4ooz\noLdAJ/1RaUf2rrPZv85m/zpXR/VucHCHeamevXuANY/sTX9/N/fc08369cXigF1dNebM2UZf3xCX\nX767YWq8ddQflXZg7zqb/ets9q9zdVzvmsyYvn5916vBas2aSUzd+jKD7Hkctdqaeqdqw3WZJUmS\ndrHp04uJPevMlzB7do2FC//DHXds4bG1m9h4wDEADzQ6lWFKkiSpgTdtfoyeTdn0GMOUJElSA8PL\n2jRjmJIkSWqkXNYGOK7RIYYpSZKkZqZPp9HgczBMSZIkVWKYkiRJqsAwJUmSVIFhSpIkqQLDlCRJ\nUgWGKUmSpAoMU5IkSRUYpiRJkiowTEmSJFVgmJIkSarAMCVJklSBYUqSJKkCw5QkSVIFhilJkqQK\nDFOSJEkVGKYkSZIqMExJkiRVYJiSJEmqwDAlSZJUgWFKkiSpAsOUJElSBYYpSZKkCgxTkiRJFXSP\ndEBEvBm4DDgqM4+ts/9s4FxgS7npe5n5wzGtUpIkqU2NGKaAucBK4Kgmx5yZmRvGpiRJkqTOMWKY\nyszbI6J3hMM+FRGbgGnAdZn5/JhUJ0mS1ObGYszUAHBFZi4D1gE/GoNzSpIkdYSuWq024kHllaml\n9cZM7XTc7sAgMCUzRz6xJElSh3stV6a6ht9ERE9E7Fm+XxIRk8pdhwBPG6QkSdJEMZqn+U4EzgIO\niIjFwHLgYuA54CpgE3BDRPwROAL46K4qVpIkqd2M6jafJEmS6nPSTkmSpAoMU5IkSRUYpiRJkioY\nzQzoGkG9JXciYjJwIfASxcD8ZzPz0tZVqXoa9G4OxUMW64D3UkwL8kDrqlQ9EXEwRe8eBA4EnsvM\nr0ZED3AF8AfgHcDizPxb6ypVPU36txx4uXwdBZyfmX9tXaWqp1H/ttt/CXBBZu7fohLHlWFqbNRb\ncuciYCAz7weIiCNaUZhGVK93lwHfzsyVEXEG8GWgrxXFqal9gVsy86cAEfFIRNwFfAK4NzNXRMRp\nwDJgQQvrVH2N+jeYmV8oty0CLgHOb12ZaqBu/zLzoXJuyh5gwjzhZpgaAw2W3PkI8KeIOBrYD/jm\n+FemkTTo3SZg+NvU/hTfvNRmMnPdTpu6KK5mzKcIxAC/Bm4az7o0Oo36NxykSrtRTAStNtOofxEx\nA/gwcCUT6EuMY6Z2nYOAWmZeC6wGbmttOXoNLgXOjoilFP8ZuERSmyuvIPZn5hPADIrb6wAvAvtE\nhP/XtbGd+je8bR/g/cDSlhWmURnuH/AksARYzATLFxPqw46zF4A15fv7gXkR0dXkeLWPO4ELM/Nz\nwHnAz1pcj5qIiJOAkzLzM+WmzcCe5fu9gOczc1sratPI6vSPiNiL4mr+xzLzH62qTSPbqX9zgH8D\nnwTOBfaIiEURMbuFJY4Lb/ONre3D0n3AwRRJ/SDgKZfZaWvb9+6tFLf6KP+dMv7laDQiYj5wQmZe\nEBEzgVnAKuB4YAXFmLhVLSxRTTTo35PANcCizHwmIj6Ymbe3tFDVVad/kzNzYblvFvDxzLyqpUWO\nE2dAHwPlkjsLKAYp30gx4HU/4EsUTxQdClxX5x6zWqxB706huOf/e+BdwI8z886WFam6yqcuVwNr\nKcLwNOB6iiuLVwIbKL7QXOzTfO2nSf/OByYBfy+3v5iZp7eqTtXXqH+ZeXN5Jerc8vU14OrM3NKy\nYseBYUqSJKkCx0xJkiRVYJiSJEmqwDAlSZJUgWFKkiSpAsOUJElSBYYpSZKkCgxTkjpCRMyKiKdH\ncdz8iLi5fH9eRPy5nE9MknYJw5SkTtHF6FahvxdYBJCZNwK5K4uSJJeTkdR2ImIP4DvAMxQzK28B\n9gf2jYhvAI+X+74O/IJiLb4TKGbQPgzoBd5enq6rPOcBFOssPkGx7ts6ipnSXwH2BjZk5hXj8PEk\nvcEYpiS1o1OAnsw8CyAiLqIIQCdm5qeHD4qIdwMfAOYBs4EDgVuBX9Y557HArZl5Zfm7XwE2Z+aS\n8udfRcQDmTmwyz6VpDckw5SkdrQOWBYRPwFuA64GZjY4diAzt1EskPtkucDqzj5UvmK7bacCmyPi\nBoqrV4PA28aofkkTiGOmJLWdzNwIvBP4LnAmRbhq9OXvX6M45XPAHcA1O22/KTMXZuZ5wGnALa+v\nYkkTmWFKUtuJiPlAb2benZlnUFyVegmYVO4/p8mvd5Wv7Q1QDErvjYhTy20/p7hFOGwZcEzl4iVN\nOF212mgejpGk8RMRxwJfBB4FeoBHM3N5RKwC/kIxaPwW4FqKJ/xuzMybImIq8C3gDGAJxSD1JcDv\ngM8CPwAOAS4Hvk9x+3A3igHuGzJz+Xh9RklvHIYpSZKkCrzNJ0mSVIFhSpIkqQLDlCRJUgWGKUmS\npAoMU5IkSRUYpiRJkiowTEmSJFVgmJIkSargv8/otkqzGVMBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d672b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(style=['b', 'r.'], figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automated Monte Carlo Tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Automated Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Storage Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename = 'data/simulation_results.h5'\n",
    "from srd_simulation_results import plot_error_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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v3pT06s3OW+9wClO667T4332b/M8WkP/ZAurfO4HS1m28wpQn9KuxMKUxJjNF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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e9a6ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_error_ratio(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 96 entries, 0 to 95\n",
      "Data columns (total 19 columns):\n",
      "ab_val_err     96 non-null float64\n",
      "abs_tol        96 non-null float64\n",
      "anti_paths     96 non-null bool\n",
      "av_val_err     96 non-null float64\n",
      "end_date       96 non-null datetime64[ns]\n",
      "error_ratio    96 non-null float64\n",
      "errors         96 non-null int64\n",
      "mo_match       96 non-null bool\n",
      "opt_prices     96 non-null float64\n",
      "paths          96 non-null int64\n",
      "rel_tol        96 non-null float64\n",
      "runs           96 non-null int64\n",
      "seed           96 non-null int64\n",
      "sim_name       96 non-null object\n",
      "start_date     96 non-null datetime64[ns]\n",
      "time_min       96 non-null float64\n",
      "time_opt       96 non-null float64\n",
      "time_sec       96 non-null float64\n",
      "time_steps     96 non-null int64\n",
      "dtypes: bool(2), datetime64[ns](2), float64(9), int64(5), object(1)\n",
      "memory usage: 13.7+ KB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "h5 = pd.HDFStore(filename, 'r')\n",
    "data = h5['sim_results']\n",
    "h5.close()\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>error_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mo_match</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>0.193750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.169097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          error_ratio\n",
       "mo_match             \n",
       "False        0.193750\n",
       "True         0.169097"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['mo_match']).mean()[['error_ratio']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>error_ratio</th>\n",
       "      <th>errors</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mo_match</th>\n",
       "      <th>anti_paths</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">False</th>\n",
       "      <th>False</th>\n",
       "      <td>0.206944</td>\n",
       "      <td>12.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.180556</td>\n",
       "      <td>10.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">True</th>\n",
       "      <th>False</th>\n",
       "      <td>0.159028</td>\n",
       "      <td>9.541667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.179167</td>\n",
       "      <td>10.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     error_ratio     errors\n",
       "mo_match anti_paths                        \n",
       "False    False          0.206944  12.416667\n",
       "         True           0.180556  10.833333\n",
       "True     False          0.159028   9.541667\n",
       "         True           0.179167  10.750000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['mo_match', 'anti_paths']).mean()[['error_ratio', 'errors']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wMvz3WcDNwEPAVQTF4MtmNiTcfD6wzN2vAS4Argu/kJKP92643WcIJqQ7D/gv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\n/x8OUWRn19vXbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e9c7cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = data.groupby(['time_steps', 'paths']).mean()[['error_ratio', 'time_opt']]\n",
    "df.plot(x='time_opt', y='error_ratio', style='bo');\n",
    "plt.xlabel('time per option valuation');\n",
    "# @savefig gl96_error_ratio_time.pdf\n",
    "plt.ylabel('error ratio');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Option Quotes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "path = 'data/'\n",
    "h5 = pd.HDFStore(path + 'vstoxx_option_quotes.h5', 'r')\n",
    "option_quotes = h5['option_quotes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 98 entries, 46219 to 46365\n",
      "Data columns (total 8 columns):\n",
      "DATE         98 non-null datetime64[ns]\n",
      "EXP_YEAR     98 non-null int64\n",
      "EXP_MONTH    98 non-null int64\n",
      "TYPE         98 non-null object\n",
      "STRIKE       98 non-null float64\n",
      "PRICE        98 non-null float64\n",
      "MATURITY     98 non-null datetime64[ns]\n",
      "TTM          98 non-null float64\n",
      "dtypes: datetime64[ns](2), float64(3), int64(2), object(1)\n",
      "memory usage: 6.9+ KB\n"
     ]
    }
   ],
   "source": [
    "option_quotes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>EXP_YEAR</th>\n",
       "      <th>EXP_MONTH</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>STRIKE</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>MATURITY</th>\n",
       "      <th>TTM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46244</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46245</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46246</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46247</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46248</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.35</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46249</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>32.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46250</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46251</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>37.5</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46252</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46253</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>42.5</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DATE  EXP_YEAR  EXP_MONTH TYPE  STRIKE  PRICE   MATURITY    TTM\n",
       "46244 2014-03-31      2014          5    C    26.0   0.60 2014-05-16  0.126\n",
       "46245 2014-03-31      2014          5    C    27.0   0.50 2014-05-16  0.126\n",
       "46246 2014-03-31      2014          5    C    28.0   0.45 2014-05-16  0.126\n",
       "46247 2014-03-31      2014          5    C    29.0   0.40 2014-05-16  0.126\n",
       "46248 2014-03-31      2014          5    C    30.0   0.35 2014-05-16  0.126\n",
       "46249 2014-03-31      2014          5    C    32.5   0.25 2014-05-16  0.126\n",
       "46250 2014-03-31      2014          5    C    35.0   0.20 2014-05-16  0.126\n",
       "46251 2014-03-31      2014          5    C    37.5   0.15 2014-05-16  0.126\n",
       "46252 2014-03-31      2014          5    C    40.0   0.10 2014-05-16  0.126\n",
       "46253 2014-03-31      2014          5    C    42.5   0.10 2014-05-16  0.126"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "option_quotes.iloc[25:35]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Calibration Procedure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Calibration Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from srd_model_calibration import *\n",
    "option_data = read_select_quotes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " kappa  theta  sigma         MSE\n",
      " 5.000 10.000  1.000     7.78606\n",
      " 6.000 12.500  9.000     0.23173\n",
      " 7.000 16.250  9.000     0.40860\n",
      " 8.000 20.000  9.000     2.54160\n",
      " 9.000 23.750  9.000     9.70521\n",
      "10.000 27.500  9.000    26.90758\n",
      "12.000 10.000  9.000     3.75104\n",
      "13.000 13.750  9.000     1.50070\n",
      "14.000 17.500  9.000     0.06953\n",
      "15.000 21.250  9.000     3.39340\n",
      "16.000 25.000  9.000    18.12298\n",
      "17.000 28.750  9.000    51.74883\n",
      "19.000 11.250  9.000     5.14886\n",
      "20.000 15.000  9.000     1.96050\n",
      " kappa  theta  sigma         MSE\n",
      "15.000 18.750  7.000     0.01356\n",
      "16.381 19.112  6.613     0.00834\n",
      "27.671 18.969  8.513     0.00831\n",
      "33.461 18.950  9.343     0.00831\n",
      "33.457 18.950  9.342     0.00831\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.008311\n",
      "         Iterations: 247\n",
      "         Function evaluations: 450\n",
      "CPU times: user 4.17 s, sys: 161 ms, total: 4.33 s\n",
      "Wall time: 4.24 s\n"
     ]
    }
   ],
   "source": [
    "%time opt = model_calibration(option_data, rel=False, mat='2014-05-16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XM+9red5c7r5o63c0nXkWKUnxdm1rD3iWmFT1CAARuVlVXwl+TURO9yYqY8ze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\nYkJjsKjiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11edd2290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_calibration_results(opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " kappa  theta  sigma         MSE\n",
      " 5.000 10.000  1.000     1.00000\n",
      " 6.000 12.500  9.000     0.12866\n",
      " 7.000 16.250  9.000     0.01167\n",
      " 8.000 20.000  9.000     0.18546\n",
      " 9.000 23.750  9.000     1.20473\n",
      "10.000 27.500  9.000     3.86826\n",
      "12.000 10.000  9.000     0.73475\n",
      "13.000 13.750  9.000     0.41208\n",
      "14.000 17.500  9.000     0.05791\n",
      "15.000 21.250  9.000     0.15684\n",
      "16.000 25.000  9.000     1.57671\n",
      "17.000 28.750  9.000     5.26237\n",
      "19.000 11.250  9.000     0.81013\n",
      "20.000 15.000  9.000     0.45062\n",
      " kappa  theta  sigma         MSE\n",
      " 5.000 20.000  5.000     0.00051\n",
      " 5.848 19.927  5.295     0.00050\n",
      "11.500 19.608  7.260     0.00049\n",
      "12.731 19.585  7.638     0.00049\n",
      "12.732 19.585  7.638     0.00049\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.000495\n",
      "         Iterations: 252\n",
      "         Function evaluations: 450\n",
      "CPU times: user 4.14 s, sys: 166 ms, total: 4.31 s\n",
      "Wall time: 4.21 s\n"
     ]
    }
   ],
   "source": [
    "%time opt = model_calibration(option_data, rel=True, mat='2014-07-18')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FjTFml+XlUN617s/JOPcxNfsO+Dru0RhjjPEFLyc/vAcgIl+rapWIZLnbO15X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\nUOEmplycCraz2LFW3v5Ah4l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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d9b9c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_calibration_results(opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Scripts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### srd_functions.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### srd_simulation_analysis.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### srd_simulation_results.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### srd_model_calibration.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:team@tpq.io\">team@tpq.io</a>\n",
    "\n",
    "**DX Analytics** |\n",
    "<a href=\"http://dx-analytics.com\">http://dx-analytics.com</a>\n",
    "\n",
    "**Quant Platform** |\n",
    "<a href=\"http://quant-platform.com\">http://quant-platform.com</a>\n",
    "\n",
    "**Python for Finance Books** |\n",
    "<a href=\"http://books.tpq.io\" target=\"_blank\">http://books.tpq.io</a>\n",
    "\n",
    "**Python for Finance Training** |\n",
    "<a href=\"http://training.tpq.io\" target=\"_blank\">http://training.tpq.io</a>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
